Molecular networks for library spectral content

Abstract

We provide a system that offers a process for generating molecular networks. [Solution] The system includes a memory for storing computer executable components and a processor for executing the computer executable components. The computer executable components include an evaluation component that performs a comparison between first and second spectral data; a scoring component that generates a spectral similarity score indicating the level of similarity between the first and second spectral data based on the comparison; a parameterization component that associates a first secondary characteristic corresponding to the first spectral data with the second spectral data or a second secondary characteristic corresponding to the second spectral data with the first spectral data based on the comparison; and a generation component that generates a grouping of spectral data including the first and second spectral data based on the spectral similarity score and the association.

Description

Technical Field

[0001] Incorporation by Reference This patent application is related to U.S. Patent Application No. __ filed on September 9, 2024 (the invention name is "Molecular Network for Library Molecular Structure Content", Attorney Docket No.: TP387483USPRV1 / TFSP137US), and the entire content thereof is incorporated herein by reference.

Background Art

[0002] Molecular networks can be used to utilize the assumption that structurally related molecules can generate similar fragmentation patterns and can thus be represented as related within the molecular network. Such molecular networks can be used to handle the large volume of library content that increases over time.

Brief Description of the Drawings

[0003] Embodiments will be readily understood by the following detailed description in conjunction with the accompanying drawings. For ease of explanation, like reference numerals denote like structural elements. Embodiments are shown by way of example and not as limitations in the figures of the accompanying drawings. [Figure 1] A block diagram of an example scientific instrument for performing one or more operations in accordance with one or more embodiments described herein is shown. [Figure 2] A flowchart of an example method of performing an operation using the scientific instrument of FIG. 1 in accordance with one or more embodiments described herein is shown. [Figure 3] A graphical user interface (GUI) that can be used for one or more executions of the methods described herein in accordance with one or more embodiments described herein is shown. [Figure 4] A block diagram of an example computing device that can perform one or more of the methods disclosed herein in accordance with one or more embodiments described herein is shown. [Figure 5] A block diagram of an example non-limiting system that can facilitate the process of generating and / or visualizing molecular networks according to one or more embodiments described herein is shown. [Figure 6] A block diagram of another example of a non-limiting system that can facilitate the process of generating and / or visualizing molecular networks according to one or more embodiments described herein is shown. [Figure 7] This document shows an example visualization of a molecular network according to one or more embodiments described herein. [Figure 8] This document shows another example of a molecular network visualization according to one or more embodiments described herein. [Figure 9] The diagram shows an interactive GUI that can be used to customize one or more parameters used by one or more embodiments described herein to generate a visualization of a molecular network according to one or more embodiments described herein. [Figure 10] A flowchart of one or more processes that can be performed by the molecular network generation system of Figure 5, according to one or more embodiments described herein, is shown. [Figure 11] Figure 6 shows another flowchart of one or more processes that can be performed by the molecular network generation system according to one or more embodiments described herein. [Figure 12] The flowchart in Figure 11 shows one or more processes that can be performed by the molecular network generation system of Figure 6 according to one or more embodiments described herein. [Figure 13] A flowchart of one or more processes that can be performed by the molecular network generation system of Figure 5, according to one or more embodiments described herein, is shown. [Figure 14] Figure 6 shows another flowchart of one or more processes that can be performed by the molecular network generation system according to one or more embodiments described herein. [Figure 15] The flowchart of Figure 14 continues, showing one or more processes that can be performed by the molecular network generation system of Figure 6 according to one or more embodiments described herein. [Figure 16] A block diagram of an example scientific instrument system capable of performing one or more of the methods described herein according to one or more embodiments described herein is shown. [Figure 17] A block diagram of an example operating environment that can incorporate embodiments of the subject matter described herein is shown. [Figure 18] This schematic block diagram shows an example computing environment in which the subjects described herein may be linked and / or implemented in at least part. [Overview of the project]

[0004] The following is a summary to provide a basic understanding of one or more embodiments described herein. This summary is not intended to identify any key points or major elements and / or to limit any particular embodiment or claim. The sole purpose of this summary is to present the concepts in a concise form prior to the more detailed descriptions described below. In one or more embodiments, the systems, computer implementations, apparatus and / or computer program products described herein can use a visualization framework to provide a plug-and-play process for generating, visualizing and / or utilizing molecular networks for various databases.

[0005] According to one embodiment, the system may include a memory for storing computer executable components and a processor for executing the computer executable components. The computer executable components may include an evaluation component that performs a comparison between first spectral data and second spectral data; a scoring component that generates a spectral similarity score indicating the level of similarity between the first spectral data and the second spectral data based on the comparison; a parameterization component that associates a first secondary characteristic corresponding to the first spectral data with the second spectral data, or associates a second secondary characteristic corresponding to the second spectral data with the first spectral data based on the comparison; and a generation component that generates a grouping of spectral data including the first spectral data and the second spectral data based on the spectral similarity score and the association.

[0006] In another embodiment, a computer implementation may include: performing a comparison between first spectral data and second spectral data by a system operably connected to a processor; generating a spectral similarity score indicating the level of similarity between the first spectral data and the second spectral data based on the comparison; associating a first secondary characteristic corresponding to the first spectral data with the second spectral data, or associating a second secondary characteristic corresponding to the second spectral data with the first spectral data based on the comparison; and generating a grouping of spectral data including the first spectral data and the second spectral data based on the spectral similarity score and the association.

[0007] In yet another embodiment, the computer program product facilitates the generation process of one or more spectral data groupings, and program instructions are executed by a processor, causing the processor to perform a comparison between first spectral data and second spectral data; generate a spectral similarity score indicating the level of similarity between the first spectral data and the second spectral data based on the comparison; associate a first secondary characteristic corresponding to the first spectral data with the second spectral data, or associate a second secondary characteristic corresponding to the second spectral data with the first spectral data based on the comparison; and generate a spectral data grouping including the first spectral data and the second spectral data based on the spectral similarity score and the association.

[0008] One or more embodiments described herein utilize a novel system that provides limited error (e.g., a very small number of unknown data points to one) when updating a spectral library and generating molecular networks based thereon. In this way, it becomes possible to classify and / or identify unknown spectra by using known spectral data and progressively expanding the spectral data, while suppressing the accumulation of errors during generation (compared to existing frameworks that use multiple unknown data points when updating a spectral library for an unknown compound, for example).

[0009] One or more embodiments described herein may be implemented within a scientific imaging device, in conjunction with such device, and / or connected to such device.

[0010] One or more embodiments disclosed herein can be applied in a plug-and-play manner to various architectures of existing spectral libraries and / or spectral data library data stores. That is, one or more embodiments described herein can generate molecular networks including visual representations of multiple chemical relationships, regardless of the data structure of the spectral library.

[0011] In one or more embodiments described herein, spectral data grouping may be generated from a molecular network and provided as one or more of the following: visuals, data, metadata, etc. Spectral data grouping may be generated based on one or more of the following: a) one or more similarity scores between pairs of spectral data, or b) one or more secondary characteristics relating to at least one of the spectral data in a pair of spectral data. That is, in one or more embodiments, spectral data grouping may be performed based on both similarity scores and secondary characteristics. In one or more other embodiments, spectral data grouping may be performed based on a first secondary characteristic and at least one other secondary characteristic.

[0012] One or more embodiments described herein can provide a dynamically adjustable visual representation of a molecular network, enabling the provision of diverse visualization formats and / or customization of visualized chemical relationships and / or properties. For example, dynamic adjustability is found in the functionality of the generated molecular network (MN), allowing the user to interact with the visual representation to change the displayed chemical classes, chemical properties, size and / or distance of each MN element, etc. Diverse visualizations may include large MN clouds, customized clouds based on one or more specified parameters, and configurations displaying multiple clouds simultaneously. Customization may be provided by using a graphical user interface (GUI) capable of representing different chemical properties and / or chemical relationships by nodes, edges, outlines of nodes and / or edges, fills of nodes and / or edges, line thickness within the cloud, distances between nodes, etc.

[0013] One or more embodiments described herein can be used to generate molecular networks that can provide diverse outputs during use of the molecular network. For example, based on visual elements in the form of an MN cloud, such as coloring, line thickness, shape, and / or distances between different elements within the MN cloud, a user subject and / or the system itself can predict one or more chemical properties and / or chemical relationships corresponding to an unknown spectrum. These one or more chemical properties and / or chemical relationships may include chemical classes, chemical applications, analogous compounds, and the like. [Modes for carrying out the invention]

[0014] The following detailed description is merely illustrative and is not intended to limit the embodiments and / or the application or use of the embodiments. Further, there is no intention to be bound by any explicit or implicit information set forth in the foregoing Summary of the Invention or the section entitled Modes for Carrying Out the Invention. One or more embodiments will be described below with reference to the drawings. Note that the same reference numerals are used throughout to indicate the same elements. In the following description, many specific details are set forth for the purpose of providing a more thorough understanding of one or more embodiments. However, it will be apparent in various instances that one or more embodiments may be practiced without these specific details.

[0015] Various operations may be described in sequence as a plurality of individual processes or operations in order to be most useful in understanding the subject matter disclosed herein. However, the order of description should not be construed as meaning that these operations necessarily depend on order. In particular, these operations may be performed in an order different from that presented. The operations described may be performed in an order different from the embodiments described. In additional embodiments, various additional operations may be performed and / or the operations described may be omitted.

[0016] Turning to the subject of molecular networking, molecular networking can organize spectral data, such as mass spectrometry / mass spectrometry (MS / MS) data, into a related network, such as a relational spectral network, thereby enabling the mapping of chemical structures based on the fragmentation patterns of chemical substances. In existing frameworks, such molecular networking can be used to discover structurally related metabolites within an experimental data set in non-targeted metabolomics experiments.

[0017] However, the analysis of non-targeted metabolomics datasets can be limited by the ability to annotate and identify metabolites. The success rate of annotation in non-targeted datasets is typically very low. Curated spectral databases of analytical standards can be used as reference points in matching fragmentation patterns. While calculating similarity scores for hits may involve automated processes, the subsequent determination and selection of optimal candidates is a difficult process, involving the challenge of sifting through lengthy hit lists or verifying mirror plots of multiple, for example, dozens of, highly-rated proposals, which can be very time-consuming.

[0018] In fact, this difficulty can be further exacerbated in existing frameworks when molecular networks and their corresponding similarity scores or other relationships are generated based on multiple unknown inputs. This may include comparing unknown spectra with multiple unknown spectra and determining relationships between unknown spectra. Generating new data using multiple unknown data points can lead to an overlay of errors (e.g., cumulative errors) in the new data, potentially resulting in subsequent identification failures and / or other problems with using molecular networks generated by existing frameworks.

[0019] Furthermore, in existing frameworks, spectral search analysis may yield hundreds of scores for analytical standards structurally similar to the query spectrum. However, verifying such lengthy tables can be difficult, error-prone, and / or time-consuming. For example, hits belonging to the same chemical family or class are often located in different positions in the corresponding results table, making interpretation difficult. The sheer number of results also contributes to the time-consuming evaluation process.

[0020] In light of one or more shortcomings of such existing frameworks, one or more embodiments described herein can provide faster and more efficient selection of the most chemically relevant analytical standards and / or accelerate the annotation process corresponding to molecular networks (MNs). In one or more cases, one or more embodiments described herein can accelerate the annotation generation process, resulting in more efficient and / or faster spectral search analysis.

[0021] In general, one or more embodiments described herein can utilize a novel system that provides limited error (e.g., a very small number of unknown data points to one data point) when updating a spectral library and generating molecular networks based thereon. In this way, it becomes possible to classify and / or identify unknown spectra by using known spectral data and progressively expanding the spectral data, while suppressing the accumulation of errors during generation (compared to existing frameworks that use multiple unknown data points when updating a spectral library for an unknown compound, for example).

[0022] Additionally and / or alternatively, one or more embodiments described herein can provide higher accuracy and / or more specific spectral data grouping using a novel system, thereby further suppressing unavailable spectral data returned based on queries and / or user-initiated and / or requested parameter adjustments and / or filtering adjustments. For example, in one or more embodiments described herein, spectral data grouping is generated from a molecular network and may be provided as one or more of the following: visual, data, metadata, etc. Spectral data grouping can be generated based on one or more of the following: a) one or more similarity scores between pairs of spectral data, or b) one or more secondary characteristics relating to at least one of the spectral data in a pair of spectral data. That is, in one or more embodiments, spectral data grouping can be performed based on both similarity scores and secondary characteristics. In one or more other embodiments, spectral data grouping can be performed based on the first secondary characteristic and at least one other secondary characteristic.

[0023] In other words, one or more embodiments described herein can provide the generation of molecular networks based on a spectral library, the updating of a spectral library based on unknown spectra, the generation of a dynamically adjustable and customizable visual representation of a molecular network cloud, and / or the generation of classification output based on a visual representation of a molecular network cloud.

[0024] One or more embodiments described herein can provide a dynamically adjustable visual representation of a molecular network, enabling the provision of diverse visualization formats and / or customization of visualized chemical relationships and / or properties. For example, dynamic adjustability is found in the functionality of the generated molecular network (MN), allowing the user to interact with the visual representation to change chemical classes, chemical properties, the size and / or distance of each MN element, etc. Diverse visualizations may include large MN clouds, customized clouds based on one or more specified parameters, and configurations displaying multiple clouds simultaneously. Customization may be provided by using a graphical user interface (GUI) capable of representing different chemical properties and / or chemical relationships by nodes, edges, outlines of nodes and / or edges, fills of nodes and / or edges, line thickness within the cloud, distances between nodes, etc.

[0025] One or more advantages may include comparing unknown spectra with molecular networks using highly curated spectral trees with different metadata classification methods, simultaneous visualization of multiple closest network families that have a structural relationship with the query spectrum, facilitating decision-making processes to accurately determine and select the best hits with high scores, and / or leveraging the chemical diversity of chemical subjects in the underlying library of the molecular network (including filtering options based on different chemical properties and / or chemical relationships in the library).

[0026] For example, one or more embodiments of molecular networking applications described herein can assist in determining and / or selecting one or more best hits from spectral similarity search results. In one or more cases, such one or more embodiments can improve the understanding of structural similarity between queries and libraries through the visualization of nodes and edges, and the nodes and edges may include different metadata available in various libraries and / or library types. The specificity and precision of spectral data grouping provided by one or more embodiments described herein cannot be provided by existing frameworks. Rather, one or more frameworks described herein can provide one or more of the following: parameter tuning, filtering, similarity score determination, etc., and by using one or more of these elements in combination, one or more frameworks can generate one or more spectral data groupings (e.g., data, metadata, visual or non-visual).

[0027] In other words, one or more embodiments described herein can be used to generate molecular networks that can provide diverse outputs during use of the molecular network. For example, based on visual elements in the form of an MN cloud, such as coloring, line thickness, shape, and / or distances between different elements within the MN cloud, a user subject or the system itself can predict one or more chemical properties and / or chemical relationships corresponding to an unknown spectrum. These one or more chemical properties and / or chemical relationships may include chemical classes, chemical uses, analogous compounds, and the like.

[0028] Furthermore, by using one or more embodiments described herein, molecular networks can be explored without querying specific spectra. By generating efficient single networks consisting of relationships such as library similarity, one or more embodiments described herein can assist in exploring and / or browsing the contents of a library and, in one or more cases, enable the visualization of the chemical diversity of the library.

[0029] Furthermore, the functionality of one or more embodiments described herein can be implemented within a scientific imaging device, in conjunction with such device, and / or connected to such device. This implementation can be applied in a plug-and-play manner to various architectures of existing spectral libraries and / or spectral data library data stores. That is, one or more embodiments described herein can generate molecular networks including visual representations of multiple chemical relationships, regardless of the data structure of the spectral library.

[0030] Next, a general description of one or more scientific instrument systems disclosed herein, and related methods, computing devices and / or computer-readable media will be provided. For example, in one or more embodiments, the system may comprise a memory for storing computer-executable components and a processor for executing the computer-executable components stored in the memory. The computer-executable components may include an evaluation component that performs a comparison between first spectral data, which includes a first mass-to-charge ratio of ions appearing in an unknown spectrum, and second spectral data, which includes a second mass-to-charge ratio of ions appearing in a known analytical spectrum in a library of known analytical spectra, and an update component that applies updates to the library of known analytical spectra based on the comparison between the unknown spectrum and the known analytical spectrum.

[0031] One or more embodiments disclosed herein can achieve performance improvements compared to existing methods, as described above. For example, at least a portion of molecular network (e.g., relationships, spectral similarity scores, etc.) can be generated based on the application of a single unknown data point and known spectral data from a specific spectral library. This makes it possible to generate a visual molecular network cloud containing a portion of the molecular network. By using one or more molecular network generation frameworks described herein, it is possible to generate and display a dynamically adjustable and customizable molecular network cloud, and / or make classification determinations related to querying unknown spectra (e.g., a single unknown data point).

[0032] Accordingly, the embodiments disclosed herein can provide improvements to scientific instrument technology (for example, improvements to the computer technology supporting such scientific instruments, among other improvements), and these embodiments can be used in a variety of fields, including but not limited to optics, signal processing, spectroscopic analysis and / or nuclear magnetic resonance (NMR).

[0033] The various embodiments disclosed herein can achieve technical advantages in the generation, visualization, and / or operation (e.g., the use of MNs) of high-information and / or accurate molecular networks by improving existing methods. That is, one or more frameworks described herein can provide a more accurate construction of molecular network and / or MN-based molecular network cloud visual representations compared to existing frameworks, thereby enabling the identification of chemical properties, chemical relationships, and / or chemical classifications for querying unknown spectra. Identification can be performed based on the underlying data / metadata of the generated MNs and / or the visual representation of the corresponding MN cloud. Querying unknown spectral data may originate from any suitable source, including, but is not limited to, scientific imaging device sources using any suitable method such as high-performance liquid chromatography (HPLC), gas chromatography (GC), ion chromatography (IC), HPLC-mass spectroscopy (HPLC-MS), GC-mass spectroscopy (GC-MS), IC-mass spectroscopy (IC-MS), nuclear magnetic resonance (NMR), Raman spectroscopy, infrared spectroscopy, and / or similar.

[0034] As described above, these technical advantages cannot be achieved by conventional and / or existing methods, and all user entities of a system including such embodiments can enjoy these advantages (for example, assistance in performing technical tasks such as generating molecular networks, visualizing molecular networks, and / or using the behavior of molecular networks to identify querying one or more unknown compounds).

[0035] Accordingly, the technical features of the embodiments disclosed herein (e.g., analyzing data that defines an unknown spectrum without using additional unknown data points) are highly unconventional in the field of materials analysis, and not limited thereto, in the fields of optics, signal processing, spectroscopic analysis and / or NMR, and the same applies to combinations of the features of the embodiments disclosed herein.

[0036] As will be further detailed herein, various aspects of the embodiments disclosed herein can improve the functionality of the computer itself. That is, the computational processing and / or user interface features disclosed herein go beyond mere information gathering and / or comparison, applying new analytical and technical techniques that change the way in which the computer analyzes material compounds. Based on generating molecular networks (for example, using a single unknown data point for each unknown spectrum query), it is possible to provide a molecular network (MN) that is more accurate, reduces compound errors, and / or allows for faster query determination compared to existing frameworks. As a result, the use of MN and / or visualization of MN (e.g., visual display of an MN cloud) may improve the speed and / or accuracy of responses related to queries. In this way, one or more non-limiting systems described herein (including molecular network generation systems) can be self-improving.

[0037] Therefore, this disclosure introduces functionality that neither existing computing devices nor humans can perform. Rather, existing computing devices are inefficient in generating molecular networks, have poor representation of relationships, and / or, due to complex errors and inappropriate representation of relationships, verifying long tables can be difficult, error-prone, and / or time-consuming. As a result, querying such existing generated MNs results in reduced accuracy, efficiency, and / or speed. Given the loss of time, energy, and / or data, operating within the scope of existing methods is impractical.

[0038] Accordingly, embodiments of the present disclosure can serve to achieve any of several technical objectives, including controlling a particular technical system or process, making decisions based on measurement results for controlling a machine, enhancing or analyzing digital audio, images, or video, separating material sources in mixed signals, generating data for reliable and / or efficient transmission or recording, providing estimates and confidence intervals for material samples, or providing high-speed processing of sensor data. In particular, the present disclosure provides technical solutions to technical challenges including, but not limited to, hologram modification, image / signal blurring, application of complex blurring techniques, and / or subsequent image reconstruction, thereby resulting in faster, more accurate, and / or more efficient processing of the generated images and, consequently, the material samples or other target compositions being imaged.

[0039] Therefore, the embodiments disclosed herein provide improvements to material analysis techniques (for example, improvements to the computer technology supporting material analysis, among other improvements).

[0040] Where used herein, the phrase “based on” should be understood to mean “at least partially based on” unless otherwise specified.

[0041] As used herein, the term “component” may refer to an atomic element, a molecular element, a phase of an atom or molecular element, or a combination thereof.

[0042] As used herein, the term “compound” may refer to a single material, multiple materials, a composition, a sample, a solution, a product, etc.

[0043] As used herein, the term "data" may include metadata.

[0044] As used herein, the terms “subject,” “requesting subject,” and “user subject” may refer to machines, devices, components, hardware, software, smart devices, entities, organizations, individuals, and / or human beings.

[0045] One or more embodiments will be described below with reference to the drawings. The same reference numerals are used throughout the drawings to indicate the same elements. In the following description, many specific details are provided for illustrative purposes to facilitate a better understanding of one or more embodiments. However, it is clear that in various cases one or more embodiments can be implemented without these specific details.

[0046] Furthermore, it should be understood that the embodiments shown in one or more figures described herein are for illustrative purposes only, and therefore the configuration of the embodiments is not limited to the systems, devices, and / or components shown in the figures, nor to any particular order, connection, and / or coupling of the systems, devices, and / or components shown in the figures.

[0047] Next, moving in particular to one or more figures, first referring to Figure 1, which shows a block diagram of a scientific instrument module 100 for performing material analysis operations using molecular network generation and / or visualization processes according to various embodiments described herein. The scientific instrument module 100 may be implemented by circuits (e.g., including electrical and / or optical components), such as programmed computing devices. The logic of the scientific instrument module 100 may be contained in a single computing device or distributed across multiple computing devices communicating with each other as needed. Examples of computing devices that can implement the scientific instrument module 100 individually or in combination are described herein with reference to computing device 400 in Figure 4, and examples of systems of interconnected computing devices that can implement the scientific instrument module 100 across one or more computing devices are described herein with reference to scientific instrument system 1600 in Figure 16.

[0048] The scientific instrument module 100 may include a first logic 102, a second logic 104, a third logic 106, and a fourth logic 108. As used herein, the term “logic” may include a device that performs a set of operations associated with the logic. For example, any of the logic elements included in module 100 can be implemented by one or more computing devices programmed with instructions to cause one or more processing devices of a computing device to perform the associated set of operations. In certain embodiments, the logic elements may include one or more non-transient computer-readable media that, when executed by one or more processing devices of one or more computing devices, have instructions to cause one or more computing devices to perform the associated set of operations. As used herein, the term “module” may refer to a set of one or more logic elements that together perform a function associated with the module. Different logic elements within a module may take the same form or different forms. For example, some logic within a module may be implemented by programmed general-purpose processing devices, while other logic within a module may be implemented by application-specific integrated circuits (ASICs). In another example, different logic elements within a module may be associated with different sets of instructions executed by one or more processing devices. A module may omit one or more of the logic elements shown in the associated drawings. For example, if a module performs only a subset of the operations described herein in relation to that module, it may include only a subset of the logic elements shown in the associated drawings.

[0049] The first logic 102 can determine, receive, retrieve, locate, download, request, measure, and / or otherwise determine data and / or metadata that define the query for an unknown spectrum. That is, the first logic 102 can obtain data that is processed and subsequently used to generate a visual representation of the molecular network cloud or to update the spectral library.

[0050] The second logic 104 can generally perform a comparison process by comparing an unknown spectrum with a known analysis spectrum in a library data store (e.g., a spectrum library), and can perform the process without comparing the unknown spectrum with other unknown spectra. In other words, the second logic 104 can use the output of the first logic 102 as a trigger for the second logic 104.

[0051] The third logic 106 can update the spectral library based on the comparison performed by the second logic 104. That is, the third logic 106 can execute using the output of the second logic 104.

[0052] The fourth logic 108 can generate, for example, spectral data groupings including data and / or metadata, and / or other data representing a visual representation of a molecular network (MN) cloud and / or a visual representation of an MN cloud. The spectral data groupings may include updates and / or are based on updates, and therefore on comparisons. That is, the fourth logic 108 can generate spectral data groupings based on the execution of the third logic 106.

[0053] Figure 2 shows flowcharts of Method 200 for performing operations using the scientific instrument module 100 according to various embodiments. Each operation of Method 200 may be described by referring to a specific embodiment disclosed herein (e.g., the scientific instrument module 100 described herein in relation to Figure 1, the GUI 300 described herein in relation to Figure 3, the computing device 400 described herein in relation to Figure 4, and / or the scientific instrument system 1600 described herein in relation to Figure 16), but Method 200 can be used to perform any suitable operation in any suitable environment. In Figure 2, each operation is shown once in a specific order, but the order of operations can be changed and / or repeated as necessary and appropriate (e.g., different operations can be performed in parallel as appropriate).

[0054] In 202, a first operation can be performed. For example, the first logic 102 of module 100 can perform a first operation 202. The first operation 202 may include determining data and / or metadata that define the query for an unknown spectrum by receiving, searching, locating, downloading, requesting, measuring, and / or other means.

[0055] In 204, a second operation can be performed. For example, the second logic 104 of module 100 can perform the second operation 204. The second operation 204 may include comparing one or more properties of the unknown spectrum (e.g., a first mass-to-charge ratio of ions appearing in the unknown spectrum) with one or more properties of the known spectrum (e.g., a second mass-to-charge ratio of ions appearing in the known spectrum).

[0056] At 206, a third operation can be performed. For example, the third logic 106 of module 100 can perform the third operation 206. The third operation 206 may include updating the spectrum library. This can be done, for example, by a write operation that adds data defining an unknown spectrum and / or comparison results output from the second operation 204.

[0057] In 208, a fourth operation can be performed. For example, the fourth logic 108 of module 100 can perform the fourth operation 208. The fourth operation 208 may include the generation of data / metadata that defines spectral data grouping (e.g., a visual representation of an MN cloud) based on the spectral library, and includes a representation of the comparison output from the second operation 204, which has been updated in the spectral library by the third operation 206.

[0058] The scientific instrument methods disclosed herein may include interactions with a user subject (e.g., via a user-local computing device 1620 as described herein in relation to Figure 16). These interactions may include providing information to the user subject (e.g., information about the operation of a scientific instrument such as the scientific instrument 1610 in Figure 16, information about a sample under analysis or other tests or measurements performed by the scientific instrument, information obtained from a local or remote database, or other information), or providing the user subject with options to input commands (e.g., to control the operation of a scientific instrument such as the scientific instrument 1610 in Figure 16, or to control the analysis of data generated by the scientific instrument), queries (e.g., to a local or remote database), or other information. In some embodiments, these interactions may be performed through a graphical user interface (GUI) including a visual display on a display device (e.g., a display device 410 as described herein in relation to Figure 4). The GUI provides output to the user subject and / or prompts the user subject to provide input (for example, via one or more input devices such as a keyboard, mouse, trackpad, or touchscreen included in other I / O devices 412 described herein in relation to Figure 4). The scientific instrument system 1600 disclosed herein may include any suitable GUI for interaction with the user subject.

[0059] Moving on to Figure 3, Figure 3 shows an example GUI 300 that can be used to perform one or more of the methods described herein, according to the various embodiments described herein. As stated above, the GUI 300 may be provided on a display device (e.g., display device 410 as described herein in relation to Figure 4) of a computing device (e.g., computing device 400 as described herein in relation to Figure 4) of a scientific instrument system (e.g., scientific instrument system 1600 as described herein in relation to Figure 16). A user subject can also interact with the GUI 300 using any suitable input device (e.g., any of the input devices included in the other I / O device 412 as described herein in relation to Figure 4) and input techniques (e.g., cursor movement, motion capture, face recognition, gesture detection, speech recognition, button presses, etc.).

[0060] The GUI300 may include a data display area 302, a data analysis area 304, a scientific instrument control area 306, and a setting area 308. The specific number and arrangement of areas shown in Figure 3 are illustrative only, and any number and arrangement of areas containing any desired features may be included in the GUI300.

[0061] The data display area 302 can display data generated by a scientific instrument (for example, the scientific instrument 1610 described herein in relation to Figure 16). For example, the data display area 302 can display one or more output results, which may include one or more spectra, one or more spectral similarity scores, one or more cloud visual displays, and / or one or more GUIs for controlling cloud visual parameters.

[0062] The data analysis area 304 can display the results of data analysis (e.g., the results of analyzing the data shown in the data display area 302 and / or other data). For example, the data analysis area 304 can display one or more output results for queries (e.g., queries for unknown compounds, spectral data grouping, etc.), such as classifications defining unknown compounds. In one or more cases, the data analysis area 304 can display a list of acquisition actions performed and / or recommended in relation to the experiment, a flowchart, or other schematic diagram. In one or more embodiments, the data display area 302 and the data analysis area 304 can be integrated within the GUI 300 (e.g., including data output from scientific instruments and analyses of some of the data in a common graph or area).

[0063] The scientific instrument control area 306 may include options that enable a user subject to control a scientific instrument (e.g., scientific instrument 1610 as described herein in relation to Figure 16). For example, the scientific instrument control area 306 may include one or more controls for customizing the cloud visual display based on the GUI 900 of Figure 9, which will be described later.

[0064] The configuration area 308 may include options that enable a user subject to control the functions and operations of GUI 300 (and / or other GUIs) and / or perform general computing operations relating to the data display area 302 and the data analysis area 304 (e.g., saving data from a storage device to a storage device 404, as described herein in relation to Figure 4, sending data to another user subject, labeling data, etc.). For example, the configuration area 308 may include one or more options for changing the color, fill, or format of any element of Figures 7 to 9 and / or other images (including, but not limited to, actual images, representative images, and / or schematic diagrams) described later.

[0065] As described above, the scientific instrument module 100 can be implemented by one or more computing devices. Therefore, referring now to Figure 4, Figure 4 shows a block diagram of a computing device 400 capable of performing some or all of the scientific instrument methods disclosed herein, according to various embodiments. In one or more embodiments, the scientific instrument module 100 can be implemented by a single computing device 400 or by multiple computing devices 400. Furthermore, as described below, the computing device 400 (or multiple computing devices 400) implementing the scientific instrument module 100 may be part of one or more of the scientific instrument 1610, user-local computing device 1620, service-local computing device 1630, or remote computing device 1640 in Figure 16.

[0066] Although the computing device 400 in Figure 4 is illustrated as having multiple components, one or more of these components may be omitted or duplicated in a manner appropriate to the application and configuration. As illustrated, these components may include one or more of the following: the processor 402, storage device 404, interface device 406, battery / power supply circuit 408, display device 410, and other input / output (I / O) devices 412.

[0067] In one or more embodiments, one or more components of the computing device 400 may be connected to one or more motherboards and housed in a chassis (e.g., made of plastic, metal, and / or other materials). In one or more embodiments, some of these components may be fabricated on a single system-on-a-chip (SoC) (for example, the SoC may include one or more processors 402 and one or more storage devices 404). Furthermore, in one or more embodiments, the computing device 400 may omit one or more of the components shown in Figure 4. In one or more embodiments, the computing device 400 may include interface circuits (not shown) for connecting to one or more components using any suitable interface (e.g., a Universal Serial Bus (USB) interface, a High Definition Multimedia Interface (HDMI®) interface, a Controller Area Network (CAN) interface, a Serial Peripheral Interface (SPI) interface, an Ethernet interface, a wireless interface, or other suitable interface). For example, the computing device 400 may omit the display device 410, but may include an interface circuit for a display device (e.g., a connector and driver circuit) to which the display device 410 can be connected.

[0068] The computing device 400 may include a processor 402 (e.g., one or more processing devices). As used herein, the term “processing device” may refer to any device or part thereof that processes electronic data from registers and / or memory and converts that electronic data into other electronic data that can be stored in registers and / or memory. The processor 402 may include one or more digital signal processors (DSPs), application-specific integrated circuits (ASICs), central processing units (CPUs), graphics processing units (GPUs), cryptographic processors (dedicated processors that execute cryptographic algorithms in hardware), server processors, or any other suitable processing devices.

[0069] The computing device 400 may include a storage device 404 (e.g., one or more storage devices). The storage device 404 may include one or more memory devices (e.g., random access memory (RAM)) (e.g., static RAM (SRAM) devices, magnetic RAM (MRAM) devices, dynamic RAM (DRAM) devices, resistive random access RAM (RRAM) devices, or conductive bridge RAM (CBRAM) devices), hard drive-based memory devices, solid-state memory devices, network drives, cloud drives, or any combination of memory devices. In one or more embodiments, the storage device 404 may include memory located on the same die as the processor 402. In such embodiments, the memory may be used as cache memory and may include, for example, embedded dynamic random access memory (eDRAM) or spin-transfer torque magnetic random access memory (STT-MRAM). In one or more embodiments, the storage device 404 includes a non-transient computer-readable medium on which instructions may be recorded that, when executed by one or more processing devices (e.g., a processor 402), cause a computing device 400 to perform suitable or partial methods of those disclosed herein.

[0070] The computing device 400 may include an interface device 406 (for example, one or more interface devices 406). The interface device 406 may include one or more communication chips, connectors, and / or other hardware and software for managing communication between the computing device 400 and other computing devices. For example, the interface device 406 may include circuitry for managing wireless communication for sending and receiving data to and from the computing device 400. The term “wireless” and its derivatives may be used to describe circuits, devices, systems, methods, techniques, communication channels, etc., that are capable of data communication using modulated electromagnetic radiation over a non-solid medium. The term does not mean that the associated devices are completely wiring-free, although in one or more embodiments the associated devices may be wiring-free. The circuitry included in interface device 406 for managing wireless communication can implement any of numerous wireless standards or protocols, including, but not limited to, the Institute of Electrical and Electronics Engineers (IEEE) standards (Wi-Fi (IEEE 802.11 family), IEEE 802.16 standard (e.g., IEEE 802.16-2005 revision)), the Long-Term Evolution (LTE) project and its revisions, updates, and / or modifications (e.g., the Advanced LTE project, the Ultra-Mobile Broadband (UMB) project (also known as "3GPP®2")), etc.). In one or more embodiments, the circuitry included in interface device 406 for managing wireless communication can operate in accordance with Global System for Mobile Communications (GSM), General-Purpose Packet Radio Services (GPRS), Universal Mobile Telecommunications System (UMTS), High-Speed ​​Packet Access (HSPA), Evolved HSPA (E-HSPA), or LTE networks.In one or more embodiments, the circuitry included in the interface device 406 for managing wireless communication may operate in accordance with GSM Evolutionary High-Speed ​​Data (EDGE), GSM EDGE Radio Access Network (GERAN), UTRAN (Universal Terrestrial Radio Access Network), or Evolutionary UTRAN (E-UTRAN). In one or more embodiments, the circuitry included in the interface device 406 for managing wireless communication may operate in accordance with Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Digitally Enhanced Cordless Communication (DECT), Evolutionary Data Optimization (EV-DO) and its derivatives, as well as any other radio protocol of 3G, 4G, 5G, and later. In one or more embodiments, the interface device 406 may include one or more antennas (e.g., one or more antenna arrays) for receiving and / or transmitting wireless communication.

[0071] In one or more embodiments, the interface device 406 may include circuitry for managing wired communications (e.g., electrical communications, optical communications, or other appropriate communication protocols). For example, the interface device 406 may include circuitry for supporting communications compliant with Ethernet technology. In one or more embodiments, the interface device 406 may support both wireless and wired communications, and / or support multiple wired communication protocols and / or multiple wireless communications protocols. For example, a first set of circuitry in the interface device 406 may be dedicated to short-range wireless communications such as Wi-Fi or Bluetooth®, and a second set of circuitry in the interface device 406 may be dedicated to long-range wireless communications such as Global Positioning System (GPS), EDGE, GPRS, CDMA, WiMAX, LTE, EV-DO, or others. In one or more embodiments, a first set of circuitry in the interface device 406 may be dedicated to wireless communications, and a second set of circuitry in the interface device 406 may be dedicated to wired communications.

[0072] The computing device 400 may include a battery / power supply circuit 408. The battery / power supply circuit 408 may include a circuit that connects one or more energy storage devices (e.g., batteries or capacitors) and / or components of the computing device 400 to a separate energy source (e.g., AC commercial power) from the computing device 400.

[0073] The computing device 400 may include a display device 410 (for example, multiple display devices). The display device 410 may include any visual indicator such as a head-up display, computer monitor, projector, touchscreen display, liquid crystal display (LCD), light-emitting diode display, or flat panel display.

[0074] The computing device 400 may include other input / output (I / O) devices 412. The other I / O devices 412 may include one or more audio output devices (e.g., speakers, headsets, earphones, alarms, etc.), one or more audio input devices (e.g., microphones or microphone arrays), location devices (e.g., GPS devices that communicate with satellite-based systems to receive the location of the computing device 400, as known in the art), audio codecs, video codecs, printers, sensors (e.g., thermocouples or other temperature sensors, humidity sensors, pressure sensors, vibration sensors, accelerometers, gyroscopes, etc.), image acquisition devices (e.g., cameras), keyboards, cursor control devices (e.g., mice, styluses, trackballs, or touchpads), barcode readers, quick response (QR) code readers, or radio frequency identification (RFID) readers, etc.

[0075] The computing device 400 may have any suitable form factor depending on its application and configuration. Examples include portable or mobile computing devices (e.g., mobile phones, smartphones, mobile internet devices, tablet computers, laptop computers, netbooks, ultrabooks, personal digital assistants (PDAs), and ultra-small personal computers), desktop computing devices, or server computing devices or other network-connected computing components.

[0076] Referring to Figures 5 and 6, in one or more embodiments, the non-limiting systems 500 and / or 600 shown in Figures 5 and 6, and / or such systems, may further include one or more computers and / or computing-based elements described herein in relation to a computing environment, such as the computing environment 1800 shown in Figure 18. In one or more embodiments described, the computers and / or computing-based elements may be used to implement one or more of the systems, devices, components and / or computer-implemented operations shown and / or described in relation to Figures 5 and / or 6, and / or in relation to other figures described herein.

[0077] First, moving to Figure 5, this figure shows an exemplary and non-limiting block diagram of system 500, which may include a molecular network generation system 502 and a library data store (DS) 535. The molecular network generation system 502 can generally facilitate the generation of molecular networks 540 by updating molecular networks 540 (e.g., via update 542) and / or generating one or more spectral data groupings 542, such as including data and / or metadata and / or molecular network visual representations 543.

[0078] In one or more embodiments, the molecular network generation system 502 may be comprised of at least a portion of a computing device 400.

[0079] It should be noted that the molecular network generation system 502 is described concisely as an introduction to a more complex and / or broader molecular network generation system 602, as shown in Figure 6. That is, further details regarding processes that may be performed by one or more embodiments described herein, in relation to the non-limiting system 600 of Figure 6, are provided below.

[0080] Continuing to refer to Figure 5, the molecular network generation system 502 may include at least a memory 504, a bus 505, a processor 506, an evaluation component 512, a scoring component 514, an update component 516, a generation component 418, and / or a parameterization component 522. The processor 506 may be identical to the processor 402, may be included in the processor 402, or may be different from the processor 402. The memory 504 may be identical to the storage device 404, may be included in the storage device 404, or may be different from the storage device 404.

[0081] One or more of the evaluation component 512, scoring component 514, update component 516, generation component 418, and / or parameterization component 522 may be operationally connected to the processor 506, which may be operationally connected to the memory 504. The bus 505 may provide such operational connections. The processor 506 may facilitate the execution of the evaluation component 512, scoring component 514, update component 516, generation component 418, and / or parameterization component 522. One or more of the evaluation component 512, scoring component 514, update component 516, generation component 418, and / or parameterization component 522 may be stored in the memory 504.

[0082] Generally, the non-limiting system 500 may use any suitable means of communication (e.g., electronic, telecommunicative, internet, infrared, optical fiber, etc.) to provide communication between the molecular network generation system 502 and / or any device associated with a user subject.

[0083] Next, with respect to a first embodiment based on a non-limiting system 500, the evaluation component 512 may perform a comparison between first spectral data (e.g., unknown spectral data 532) including a first mass-to-charge ratio of an ion shown in an unknown spectrum 531 and second spectral data (e.g., known spectral data 538) including a second mass-to-charge ratio of an ion shown in a known analytical spectrum 534. This known analytical spectrum 534 is included in a library of known analytical spectra (e.g., a spectral library or library data store 535).

[0084] The scoring component 514 can generally determine whether there are other known spectra (e.g., known analysis spectra 534) that should be compared with the unknown spectrum 531.

[0085] The update component 516 may generally apply the update 542 to the spectral library (e.g., library data store 535) based on a comparison between the unknown spectrum 531 and the known analysis spectrum 534.

[0086] As a result of these components, the molecular network 540 can be updated based on a single unknown data point (e.g., unknown spectral data 532) without comparing the unknown spectral data 532 with any additional unknown spectral data. As described above, this can help suppress the accumulation of errors and the decrease in accuracy in the generation (which may include updates) of the molecular network 540. In other words, the molecular network generation system 502 can facilitate the process of generating at least part of the molecular network (MN) 540 by updating the library data store 535, and by extension the molecular network 540 using the library data store 535, based on the update 542.

[0087] As a summary of the above components and their functions, we now briefly refer to Figure 10, which shows a flowchart of an exemplary and non-limiting method 1000 for performing a process that can facilitate the generation and / or renewal of molecular networks (MNs) according to one or more embodiments such as the non-limiting system 500 of Figure 5. Although the non-limiting method 1000 is described in relation to the non-limiting system 500 of Figure 5, the non-limiting method 1000 is also applicable to other systems described herein, such as the non-limiting system 600 of Figure 6. For brevity, descriptions of similar elements and / or repetitions of processes used in each embodiment are omitted.

[0088] In 1002, the non-limiting method 1000 may include a process by which a system (e.g., evaluation component 512) compares first spectral data (e.g., unknown spectral data 532 and / or known spectral data 538). The first spectral data includes a first mass-to-charge ratio of ions shown in an unknown spectrum (e.g., unknown spectrum 531), and the second spectral data (e.g., known spectral data 538) includes a second mass-to-charge ratio of ions shown in a known analytical spectrum contained in a library of known analytical spectra (e.g., known analytical spectrum 534) (e.g., library data store 535).

[0089] In step 1004, the non-restrictive method 1000 may determine, by the system (e.g., the scoring component 514), whether there are additional known spectra in the library to compare with the unknown spectrum. If yes, the non-restrictive method 1100 may return to step 1002 and continue processing. If no, the non-restrictive method may proceed to step 1006.

[0090] In 1006, a non-limiting method 1000 may apply updates to a library of known analysis spectra based on a comparison between unknown spectra and known analysis spectra by a system (e.g., update component 516).

[0091] Next, we move to a second embodiment based on the non-limiting system 500. The evaluation component 512 may perform a comparison between the first spectral data and the second spectral data. The first spectral data may be either unknown spectral data 532 or known spectral data 538. The second spectral data may be either unknown spectral data 532 or known spectral data 538. For example, two unknown spectral data 532 may be compared, two known spectral data 538 may be compared, and / or unknown spectral data 532 may be compared with known spectral data 538. As described above, the known spectral data 538 may be obtained from the library data store 535 or other information data stores used by the molecular network 540, which is at least partially supported by the molecular network generation system 502.

[0092] The scoring component 514 may generate a spectral similarity score 544 indicating the level of similarity between the first spectral data and the second spectral data, based on the comparison performed by the evaluation component 512.

[0093] The parameterized component 522 may, based on the comparison, associate a first secondary characteristic 545 corresponding to the first spectral data with the second spectral data, or associate a second secondary characteristic 545 corresponding to the second spectral data with the first spectral data.

[0094] The generation component 518 may generate a spectral data grouping (e.g., spectral data grouping 542) that includes first spectral data and second spectral data. The spectral data grouping 542 may be based on the association of spectral similarity scores 544 with first secondary characteristics 545 or second secondary characteristics 545.

[0095] As a result, the spectral data grouping 542 is generated from the molecular network 540 and may be provided as one or more of the following: a visual representation (e.g., molecular network visual representation 543), data, metadata, etc. The spectral data grouping 542 may be generated based on one or more of the following: a) one or more similarity scores 544 in a pair of spectral data 532, 538, or b) one or more secondary characteristics 545 corresponding to at least one of the spectral data 532, 538 in that pair. That is, in one or more embodiments, the spectral data grouping 542 may be composed of the similarity scores 544 and the secondary characteristics 545. In one or more other embodiments, the spectral data grouping 542 may be composed of a first secondary characteristic 545 and at least one other secondary characteristic 545.

[0096] As another summary of the above components and their functions, we now briefly refer to Figure 13, which shows a flowchart of an exemplary and non-limiting method 1300 that may facilitate the process of generating and / or updating spectral data groupings from molecular networks according to one or more embodiments (e.g., non-limiting system 500 in Figure 5). Although non-limiting method 1300 is described in relation to non-limiting system 500 in Figure 5, non-limiting method 1300 may also be applied to other systems described herein, such as non-limiting system 600 in Figure 6. For brevity, descriptions of similar elements and / or repetitions of processes used in each embodiment are omitted.

[0097] In 1302, the non-limiting method 1300 may include a system (e.g., evaluation component 512) performing a process of comparing first spectral data (e.g., unknown spectral data 532 or known spectral data 538) with second spectral data (e.g., unknown spectral data 532 or known spectral data 538).

[0098] In step 1304, the non-limiting method 1300 may include a process by the system (e.g., scoring component 514) determining whether there are additional known spectra in a library (e.g., library data store 535) for comparison with the unknown spectrum. If yes, the non-limiting method 1300 may return to step 1302 and continue processing. If no, the non-limiting method may proceed to step 1306.

[0099] In 1306, the non-limiting method 1300 may include a process in which a system (e.g., a scoring component 514) generates a spectral similarity score (e.g., a spectral similarity score 544) indicating the degree of similarity between the first spectral data and the second spectral data.

[0100] In 1308, the non-limiting method 1300 may include, based on the comparison result, a process by which the system (e.g., parameterization component 522) associates a first secondary characteristic (e.g., secondary characteristic 545) corresponding to a first spectral data with a second spectral data, or a process by which the system (e.g., parameterization component 522) associates a second secondary characteristic (e.g., secondary characteristic 545) corresponding to a second spectral data with a first spectral data.

[0101] In 1310, the non-limiting method 1300 may include, by a system (e.g., a generating component 518), generating a group of spectral data including first spectral data and second spectral data based on spectral similarity scores and associations.

[0102] Moving on to Figure 6, a non-limiting system 600 is illustrated, which may include a molecular network generation system 602 and a library data store (DS) 635. For brevity, descriptions of similar elements and / or repetitions of processes used in each embodiment are omitted. The descriptions relating to the embodiment in Figure 5 are also applicable to the embodiment in Figure 6. Similarly, the descriptions relating to the embodiment in Figure 6 are also applicable to the embodiment in Figure 5.

[0103] Generally, the molecular network generation system 602 can facilitate the process of generating at least some molecular networks (MNs) 640 (for example, by updating the library data store 635). This allows molecular networks 640 that utilize the library data store 635 to be generated based on the update 642. In one or more embodiments, the MN generation system 602 can further facilitate the process of generating and / or displaying spectral data groupings 642 (for example, molecular network visual representations 643, such as the MN cloud visual representation 750 shown in Figure 7 later).

[0104] In one or more embodiments, the molecular network generation system 602 may be at least partially comprised of a computing device 400.

[0105] One or more communications between one or more components of a non-exclusive system 600 may be provided by wired and / or wireless means, including, but not limited to, using a cellular network, a wide area network (WAN) (e.g., the Internet), and / or a local area network (LAN). Appropriate wired or wireless technologies to support communications include, but are not limited to, Wireless Fidelity (Wi-Fi), Global System for Mobile Communications (GSM), Universal Mobile Telecommunications System (UMTS), Global Interoperability Microwave Access (WiMAX), Enhanced General-Purpose Packet Radio Services (enhanced GPRS), 3G Partnership Project (3GPP®) Long-Term Evolution (LTE), 3G Partnership Project 2 (3GPP2) Ultra Mobile Broadband (UMB), High-Speed ​​Packet Access (HSPA), Zigbee® and other 802.XX wireless technologies and / or legacy communication technologies, BLUETOOTH®, Session Initiation Protocol (SIP), ZIGBEE®, RF4CE protocol, Wireless HART protocol, and 6LoWPAN (Low Power Wireless Area). This may include IPv6 on networks, Z-Wave, advanced and / or adaptive networking technologies (ANT), ultra-wideband (UWB) standard protocols, and / or other proprietary and / or non-proprietary communication protocols.

[0106] The molecular network generation system 602 can be associated with a cloud computing environment, such as being accessible via a cloud computing environment like the cloud computing environment 1700 shown in Figure 17.

[0107] The molecular network generation system 602 may include multiple components. These components may include memory 604, a processor 606, a bus 605, an acquisition component 610, an evaluation component 612, a scoring component 614, an update component 616, a generation component 618, a display component 620, a parameterization component 622, and / or an execution component 624. Using these components, the molecular network generation system 602 may update the molecular network 640 in response to an unknown spectral query 630, generate spectral data groupings 642 such as an MN visual display 643, and / or output a query response 692.

[0108] Next, the processor 606, memory 604, and bus 605 of the molecular network generation system 602 will be described. For example, in one or more embodiments, the molecular network generation system 602 may include a processor 606 (e.g., a computer processing unit, a microprocessor, a classical processor, a quantum processor, and / or a similar processor). In one or more embodiments, the components associated with the molecular network generation system 602, whether or not they refer to one or more figures of one or more embodiments described herein, may include one or more computer and / or machine-readable, writable, and / or executable components and / or instructions that can be executed by the processor 606, and the execution of one or more processes defined by such components and / or instructions may be provided. In one or more embodiments, the processor 606 may include an acquisition component 610, an evaluation component 612, a scoring component 614, an update component 616, a generation component 618, a display component 620, a parameterization component 622, and / or an execution component 624.

[0109] In one or more embodiments, the molecular network generation system 602 may include a computer-readable memory 604 which can be operably connected to a processor 606. The memory 604 may store computer-executable instructions that, when executed by the processor 606, cause the processor 606 and / or one or more other components of the molecular network generation system 602 (e.g., an acquisition component 610, an evaluation component 612, a scoring component 614, an update component 616, a generation component 618, a display component 620, a parameterization component 622, and / or an execution component 624) to perform one or more operations. In one or more embodiments, the memory 604 may store computer-executable components (e.g., an acquisition component 610, an evaluation component 612, a scoring component 614, an update component 616, a generation component 618, a display component 620, a parameterization component 622, and / or an execution component 624).

[0110] The molecular network generation system 602 and / or its components may be coupled to each other via bus 605 in communicative, electrical, operational, optical and / or other ways, as described herein. Bus 605 may include one or more of the following: memory bus, memory controller, peripheral bus, external bus, local bus, quantum bus and / or other types of buses, and may employ one or more bus architectures. One or more examples of these bus 605 may be used.

[0111] In one or more embodiments, the molecular network generation system 602 may be coupled via a network or the like to one or more external systems (e.g., an electrical output generation system not shown, one or more output targets and / or output target control devices), a source and / or device (e.g., a classical and / or quantum computing device, a communication device and / or similar device) by communicative, electrical, operational, optical and / or similar functions. In one or more embodiments, one or more components of the molecular network generation system 602 and / or a non-limiting system 600 may reside in the cloud and / or locally in a local computing environment (e.g., a specific location).

[0112] In addition to the processor 606 and / or memory 604 described above, the molecular network generation system 602 may include one or more computer and / or machine-readable, writable and / or executable components and / or instructions that, when executed by the processor 606, can provide the execution of one or more processes defined by such components and / or instructions.

[0113] Next, additional components of the molecular network generation system 602 (e.g., acquisition component 610, evaluation component 612, scoring component 614, update component 616, generation component 618, display component 620, parameterization component 622, and / or execution component 624) will be described. In general, the molecular network generation system 602 can perform a series of processes, which may be divided into various steps, not limited to: analysis of unknown spectral queries, updating of molecular networks 640, generation of spectral data grouping 642, generation of MN cloud visual display 750, and / or operation of MN 640 to obtain query responses 692.

[0114] First, it should be noted that in one or more embodiments, the acquisition component 610, evaluation component 612, scoring component 614, update component 616, generation component 618, display component 620, parameterization component 622, and / or execution component 624 may be implemented independently in a configuration that does not include one or more of the acquisition component 610, evaluation component 612, scoring component 614, update component 616, generation component 618, display component 620, parameterization component 622, and / or execution component 624. In addition, and / or alternatively, the acquisition component 610, evaluation component 612, scoring component 614, update component 616, generation component 618, display component 620, parameterization component 622 and / or execution component 624 may be comprised of a higher-order analysis component 603, and one or more of the functions of the acquisition component 610, evaluation component 612, scoring component 614, update component 616, generation component 618, display component 620, parameterization component 622 and / or execution component 624 described below may be comprised of a higher-order analysis component 603 It may be performed by and / or the acquisition component 610, evaluation component 612, scoring component 614, update component 616, generation component 618, display component 620, parameterization component 622 and / or execution component 624 may be omitted, and the higher-order analysis component 603 may perform one or more of the functions of one or more omitted acquisition components 610, evaluation component 612, scoring component 614, update component 616, generation component 618, display component 620, parameterization component 622 and / or execution component 624 as described below.

[0115] First, the acquisition component 610 will be described. This acquisition component 610 can generally perform acquisitions (e.g., acquisition, localization, identification, request, download, etc.). Spectral queries 630 corresponding to unknown spectra 631 and / or known spectra 634. In one or more instances, the spectral query 630 may include unknown spectral data 632 indicating the unknown spectrum 631. In one or more embodiments, the acquisition component 610 may intercept, read and / or copy query signals, communications, etc. (e.g., including spectral queries 630) destined for the molecular network 640 (e.g., if MN640 uses a processor such as processor 606 or another processor). For example, the spectral query 630 may be in any suitable format, may include data and / or metadata, and may include spectra or underlying spectral data. For example, the spectral query 630 may include unknown spectral data 632. The unknown spectral data 632 may include data defining at least the mass-to-charge ratio of the ions shown in the unknown spectrum 631 and / or the underlying data of the spectrum.

[0116] Generally, the evaluation component 612 may perform a process of comparing first spectral data, which includes a first mass-to-charge ratio of the ion shown in each spectrum, with second spectral data, which includes a second mass-to-charge ratio of the ion shown in the second spectrum. In one or more cases, this may include performing a process of comparing first unknown spectral data 632, which includes a first mass-to-charge ratio of the ion shown in each unknown spectrum 631, with second known spectral data 638, which includes a second mass-to-charge ratio of the ion shown in a second known analytical spectrum 634 contained in a library of known analytical spectra (e.g., a spectral library or library data store 635). In other embodiments, unknown spectral data 632 may be compared with unknown spectral data 632, or known spectral data 638 may be compared with known spectral data 638.

[0117] That is, the evaluation component 612 may generate queries and / or commands for accessing the library data store 635, and may obtain known spectral data 638 corresponding to one or more known analytical spectra 634. In one or more embodiments, the known analytical spectra 634 may be standard-based analytical spectra 634. In one or more embodiments, the known analytical spectra 634 may consist of queries previously related to unknown spectra. In one or more embodiments, a particular group of one or more known analytical spectra 634 may be adopted, for example, specified by a user entity using a user entity device that can communicate with the unrestricted system 600. For example, the user entity may have predictions, inferences, or hypotheses regarding the classification and / or other properties of the compounds underlying the spectra related to the query 630. For this reason, the system (e.g., the evaluation component 612) may configure the specified grouping based on the predictions, inferences, or hypotheses.

[0118] Additionally, and / or alternatively, the evaluation component 612 may use one or more other secondary properties 645 to indicate, define, and / or combine the unknown spectrum 631 and / or the known analytical spectrum 634 in order to perform the comparison. This may, in addition, and / or alternatively, include ion type, number of ions, ionic strength, activation energy, given energy level, and / or reaction time, and does not necessarily have to include mass-to-charge ratio. This may, in addition, and / or alternatively, include one or more secondary properties 645 from any of the parameter classes shown in Figure 9 (e.g., secondary property classes of general parameters 902, similarity score criteria 904, multi-class or hierarchical classification 906, visualization 908, and node ion visualization 910, which are described in detail below).

[0119] It should be noted that in one or more cases, the comparison performed by the evaluation component 612, and consequently the scoring performed by the scoring component 614 and the updating performed by the update component 616, can be performed without additionally comparing the unknown spectrum 631 with one or more other unknown spectra 631. In this way, the evaluation component 612, and consequently the MN generation system 602, can provide limited error (e.g., a very small number to one data point of unknown data) when updating the spectral library and generating molecular networks based thereon. In this way, it is possible to classify and / or identify unknown spectra by using known spectral data and gradually expanding the spectral data, while suppressing the accumulation of errors during generation (compared to existing frameworks that use multiple unknown data points when updating a spectral library for an unknown compound, for example).

[0120] Based at least on an initial comparison of a pair of spectra by the evaluation component 612, the scoring component 614 can generally determine whether there are other spectra (e.g., known analysis spectra 634) to compare with the spectrum related to the spectrum query 630. This may be based on a random determination of the number of known analysis spectra 634 to be analyzed, specific known spectra 634 for a given grouping, and / or requests controlled by the user.

[0121] Furthermore, based on at least the initial comparison of a pair of spectra by the evaluation component 612, the scoring component 614 can generally generate a spectral similarity score 704 (Figures 6 and 7) indicating the degree of similarity between the compared spectra. In fact, the scoring component 614 can generate a spectral similarity score for each pair of spectral data (e.g., unknown spectral data 632 and / or known spectral data 638) compared with each other by the evaluation component 612, based on the respective comparison results. For example, the spectral similarity score 704 may indicate the similarity between an ion with a first mass-to-charge ratio and an ion with a second mass-to-charge ratio. As another example, the scoring component 614 can generate a spectral similarity score 704 showing a comparison between unknown spectral data 632 and known spectral data 638.

[0122] In one or more embodiments, the scoring component 614 may use scoring algorithms, programs, code, and / or applications such as the cosine method, Tanimoto method, Euclidean method, Dice method, HighChem-HighRes algorithm, and / or algorithms based on the National Institute of Standards and Technology (NIST).

[0123] For example, a spectral similarity score of 704 may be based on a scale from 0 to 1, where 0 means there is no similarity between the compared spectra, and 1 means there is perfect similarity between the compared spectra. Any division or subdivision between 0 and 1 may be used, for example, a number with any number of decimal places may be applied.

[0124] Additionally, and / or alternatively, the evaluation component 612 may further associate secondary characteristics 645 of one spectral data with another spectral data based on the comparison performed by the evaluation component 612 between the first spectral data and the second spectral data.

[0125] That is, the evaluation component 612 may identify identification metadata 646 (e.g., ID metadata 646) associated with the spectral data 632, 638, and the metadata 646 may define secondary characteristics 645. Such secondary characteristic metadata 646 may be stored together with the respective spectral data 632, 638, and / or separately. In one or more embodiments, if the unknown spectral data 632 is associated with the spectral query 630, the secondary characteristic metadata 646 may be included in the unknown spectral data 632, and / or exist separately.

[0126] This association can be performed on multiple different spectral pairs (e.g., spectral data pairs), as well as on the comparison of spectral data pairs and the generation of spectral similarity scores for each. For example, it can be performed on all combinations of spectral data associated with spectral query 630 and each spectral data contained in and / or used by molecular network 640 (e.g., contained in library data store 635).

[0127] Secondary properties 645 may be based on and / or include any one or more of the properties provided in Figure 9, and / or may be based on and / or include any one or more of the properties provided above and / or below. For example, secondary properties 645 may be based on and / or include one or more physical properties, chemical properties, compound classification, compound use classification, substructure similarity, chemical structure description classification or higher classification, toxicological properties, physicochemical properties, metabolic pathways, enzymatic reactions, biological reactions, enzymes or catalysts, or organisms or tissues, fragmentation dynamics, fragmentation dynamics degradation curves, optimal energy, collision energy, chemical formula, neutrality loss, peak number, commercial applications, household uses and / or industrial applications. In one or more embodiments, one or more such properties may be provided by the system as being associated with a parameter 906 (Figure 9) for multi-class or hierarchical classification.

[0128] In one or more examples, the evaluation component 612 may associate a first secondary characteristic 645 corresponding to the first spectral data 632, 638 with the second spectral data 632, 638 based on a spectral similarity score 704 that defines the similarity level between the first spectral data and the second spectral data. Additionally, and / or alternatively, the evaluation component 612 may associate a second secondary characteristic 645 corresponding to the second spectral data 632, 638 with the first spectral data 632, 638 based on a spectral similarity score 704 that defines the similarity level between the first spectral data and the second spectral data.

[0129] For example, based on a spectral similarity score that satisfies (e.g., achieves and / or exceeds) a score threshold, the evaluation component 612 may determine that a secondary characteristic 645 of one spectral data in a pair of spectral data corresponding to the spectral similarity score 704 is also associated with the other spectral data in the pair.

[0130] Additionally and / or alternatively, in one or more examples, the evaluation component 612 may associate a first secondary characteristic 645 corresponding to the first spectral data 632, 638 with the second spectral data 632, 638 based on a plurality of spectral similarity scores 704 defining the respective similarity levels between the first spectral data and a plurality of second spectral data (for example, for a plurality of spectra such as a known analytical spectrum 634 derived from a library data store 635 used by the molecular network 640). Additionally and / or alternatively, the evaluation component 612 may associate a second secondary characteristic 645 corresponding to the second spectral data 632, 638 with the first spectral data 632, 638 based on a plurality of spectral similarity scores 704 defining the respective similarity levels between the first spectral data and a plurality of second spectral data (for example, for a plurality of spectra such as a known analytical spectrum 634 derived from a library data store 635 used by the molecular network 640).

[0131] For example, based on the number of spectral similarity scores 704 that satisfy a score threshold (e.g., are achieved and / or exceeded), or based on the sum of spectral similarity scores 704 that satisfy the score threshold, or based on the sum of all spectral similarity scores 704 associated with the first spectral data that satisfy the score threshold, the evaluation component 612 may determine to associate a secondary characteristic 645 of one spectral data in a pair of spectral data corresponding to at least one spectral similarity score 704 with the other spectral data in the pair. Additional associations may also be performed based on this.

[0132] Additionally and / or alternatively, in one or more examples, the evaluation component 612 may associate a first secondary characteristic 645 corresponding to the first spectral data 632, 638 with the second spectral data 632, 638 on the basis that the second spectral data includes at least a predetermined number of spectral data (e.g., known or unknown spectral data; for example, a known analytical spectrum 634 derived from a library data store 635 used by the molecular network 640) that satisfy the comparison criteria.

[0133] For example, at least a first quantity consisting of second spectral data (e.g., a plurality of known analytical spectral data 638) may each be associated with a second secondary characteristic 645. The second spectral data of the first quantity may be compared with the first spectral data by the evaluation component 612 and determined to have verified similarity (e.g., having a predetermined similarity score 704; for example, each score 704 meets a threshold and / or the threshold is met based on the aggregation of each similarity score 704). Based on the achievement and / or exceedance of the first quantity (e.g., a quantity as a threshold to be met, as described herein), the evaluation component 612 may associate the second secondary characteristic with the first spectral data, or associate the first secondary characteristic with the second spectral data.

[0134] Additionally, and / or alternatively, in one or more embodiments, the first secondary characteristic 645 may be associated with the second spectral data only if the first spectral data is associated with a high priority (e.g., if a priority threshold is met). This high priority may be based on the respective identification metadata 646 corresponding to the first spectral data.

[0135] Additionally and / or alternatively, in one or more embodiments, one or more secondary characteristics 645 may be associated prior to or in place of one or more other secondary characteristics 645. Such determination may be made by the system, for example, based on historical data retrieved from data store 635, and / or based on a user-initiated selection using a computer device communicatively connected to the non-limiting system 600. For example, a secondary characteristic 645 relating to fragmentation dynamics may be associated prior to a secondary characteristic 645 relating to collision energy. If the metadata corresponding to the spectral data does not include a first option (e.g., fragmentation dynamics), then a second option (e.g., collision energy) may take precedence.

[0136] Any two or more of the above examples can be performed on the same first spectral data 632, 638. Any two or more of such associations can be performed at least partially in parallel with each other.

[0137] Next, the update component 616 will be described. Through the use of the scoring component 614, and the subsequent update of the library data store 635 underlying the MN640, and ultimately the update of the MN640 itself, the shortcomings of the existing system, namely the improper representation of relationships, can be corrected at least partially and / or completely. This can lead to improvements in accuracy, efficiency, and / or speed when processing queries 630 to the MN640 or the MN generation system 602.

[0138] In other words, the update component 616 may generally apply update 642 to the spectral library (e.g., library data store 635) based on a comparison between the first spectral data and the second spectral data. In one or more embodiments, the update component 616 may also and / or alternatively apply update 642 to MN640 and / or instruct MN640 to perform the update based on the library data store 635.

[0139] In one or more embodiments, update 642 may include an unknown spectrum 631, unknown spectral data 632, one or more generated spectral similarity scores 704, one or more associated secondary properties 645, and / or a temporary identifier for the unknown compound underlying the unknown spectral data 632.

[0140] Next, the generating component 618 is described. This generating component 618 can generally generate a spectral data grouping 642 (also called spectral data grouping 642) containing first spectral data and second spectral data, based on the association of spectral similarity scores 704 and one or more secondary characteristics 645. The spectral data grouping 642 may be a list, matrix, log, or any other form of data, metadata and / or labels, which defines a set of spectral data that may include any combination of known and / or unknown spectral data, and which the generating component 618 has determined to be related to each other, i.e., have a relationship. The spectral data grouping 642 may be provided to the user subject in any suitable format, such as a list, matrix, log, etc. (e.g., sent to and / or made available on the user subject's computer device). In one or more cases, the grouping of spectral data 642 may be used, in addition and / or alternatively, by the display component 620 to generate molecular network visual displays 643, such as the cloud visual displays 700 / 750 described below.

[0141] The relationships may generally be constructed based on a combination of spectral similarity scores 704 and secondary characteristics 645 associated with the spectral data included in the spectral data grouping 642. In one or more examples, the spectral data grouping 642 may be constructed based on spectral similarity scores 704 in a first predetermined range and one or more secondary characteristics 645 in a second predetermined range. In one or more examples, the second predetermined range may be based on the first predetermined range, or vice versa.

[0142] In one or more cases, the selection of a first predetermined range and / or a second predetermined range may be based on the determination by the generating component 618 and / or the data associated with the spectral query 630. Additionally and / or alternatively, in one or more cases, the selection of the first predetermined range may be based on the highest spectral similarity score 704 associated with the first spectral data (for example, the first spectral data may correspond to the spectral query 630). Additionally and / or alternatively, in one or more cases, the selection of the second predetermined range may be based on a secondary characteristic 645 associated by the evaluation component 612 (the secondary characteristic 645 may be associated with the first spectral data or with the second spectral data).

[0143] Next, moving to Figure 700 in Figure 7, and continuing to refer to Figure 6, Figure 700 shows an exemplary visualization of a portion of MN640 based on the generation of a single spectral similarity score 704, which can be generated by the generation component 618 and the display component 620. For example, the generation component 618 may generate data (e.g., the underlying data) that defines the cloud visual display figures 700 / 750. The display component 620 may then generate visual data and / or display the cloud visual display figures 700 / 750 on any suitable GUI, display, etc., which can communicate with the MN generation system 602 and / or more generally, the unspecified system 600.

[0144] The generation of basic data by the generation component 618 can be performed using the known spectral data 638 and unknown spectral data 632 for the unknown spectrum 631 in the library data store 635. The generation component 618 can generate communication documents, labels, identifiers, metadata, etc., corresponding to the relationship between the unknown spectrum 631 and the known analysis spectrum 634, and this relationship may correspond to the respective spectral similarity scores 704.

[0145] The display component 620 may, based on the generation, generate visual data for generating a visualization of MN640 (or a portion thereof). This may include generating visualization data to represent the unknown spectrum 631 as node 702U, generating visualization data to represent the known analysis spectrum 634 as node 702K, and generating visualization data to represent the spectral similarity score 704 as edges 703 extending between each node 702. The edges 703 may include text containing the numerical value of the spectral similarity score 704 in their vicinity, adjacent parts, connections, etc., to facilitate visual reference by the user.

[0146] Moving on to Figure 750, which shows a visual representation of the MN cloud in cloud format, this includes Figure 700 and is configured to represent multiple additional relationships in cloud format between the unknown spectrum 631 (as node 702U) and multiple additional known analysis spectra 634 (as additional nodes 702).

[0147] Nodes 702 and / or edges 703 may be supplemented with metadata (but not limited to) including compound class, name, classification system, chemical family, biochemical activity, and / or hydrophobicity, by the generation component 618 (for generating the underlying data) and / or display component 620 (for visualizing the underlying data). This metadata may be reflected in the size, shape, color, fill color, fill pattern, border color, border thickness, length, and / or position of nodes 702 and / or edges 703.

[0148] As shown in MN Cloud Visual Display 750, the edge 703 of the first generation 711 may extend from an unknown node 702U to a first group of known nodes 702K. Similarly, as shown in MN Cloud Visual Display 750, the edge of the second generation 712 may extend from a first group of known nodes 702K to a second group of known nodes 702K. In fact, relationships between one or more generations can be visualized.

[0149] The identifier may be used for one or more nodes 702 or edges 703. For example, a text identifier based on the associated secondary characteristic 645 may be used for node 702.

[0150] Furthermore, such identifiers may be provided in a format other than text. For example, node 702 and / or edge 703 may be supplemented by metadata (but not limited to) including compound class, name, classification system, chemical family, biochemical activity, and / or hydrophobicity, by the generation component 618 (for generating the underlying data) and / or display component 620 (for visualizing the underlying data). The metadata may be based on the secondary properties 645 described above and / or below in this specification and may reflect visual attributes, such as the size, shape, color, fill color, fill pattern, border color, border thickness, length, and / or position of node 702 and / or edge 703. For example, a first visual attribute may include a colored edge 703, and a second visual attribute may include a colored border of node 702.

[0151] To provide visual attributes, the display component 620 may evaluate identification metadata 646 further associated with secondary characteristics 645 associated with spectral data 632, 638 used by the molecular network 640.

[0152] Briefly referring to the schematic diagram in Figure 8, in one or more embodiments, the node 702 and / or edge 703 may be clickable, operable, interactive, etc., thereby causing changes in the visualized library spectrum. For example, selecting node 702 may make node 702 the center of the cloud visual display 750 and / or display a text box containing corresponding information (e.g., chemical properties, classification, relationship, etc.). As another example, selecting node 702 may display a text box containing a definition of the node 702's properties (e.g., color, thickness, fill, pattern, etc.). For example, selecting edge 703 may display a text box containing the justification or underlying calculations that define the spectral similarity score 704 and / or other properties of edge 703 (e.g., color, thickness, etc.).

[0153] Furthermore, as shown in Figure 8, known nodes 702K can be visually grouped by spacing them apart, using different patterns, changing the border color, etc. These groups 810, 820 may be any appropriate number and may be configured based on any appropriate parameters or a combination of parameters, which will be discussed later in relation to Figure 9.

[0154] In relation to either Figure 7 or Figure 8, in one or more embodiments, the selection of one or more subgroups of known analytical spectra 634 (and their corresponding nodes 702K) may be made from the contents of the entire spectral library or a portion of the spectral library. The selection may take into account collision-induced dissociation (CID), high-energy C-trap dissociation (HCD), ultraviolet photodissociation (UVPD), or any other activation energy and a predetermined energy level or reaction time, and / or may be based on any other appropriate correspondence (e.g., chemical family, classification, property and / or relationship). In one or more subgroups, the known analytical spectra 634 may be those derived from a predetermined energy level. If a known analytical spectrum 634 is not available at a predetermined and / or specified property, such as an activation energy, the generating component 618 may use the nearest available energy level. For example, if CID 20 is not available, CID 15, 25, or 30 may be used.

[0155] Furthermore, in relation to either Figure 7 or Figure 8, in one or more embodiments, the generation component 618 and the display component 620 may provide simultaneous visualization of multiple nearest network families (e.g., multiple MN cloud visual displays 750) that exhibit spectral relationships with respect to the query spectrum. This may enable the parallel visualization of different clouds, with or without the presence of unknown spectral nodes 702U.

[0156] Next, moving to Figure 9, which shows a schematic diagram 900 of an interactive panel GUI, the GUI may be used to edit one or more parameters 902, 904, 906, 908 and / or 910 (but not limited to these) used to generate a visualization of the molecular network by one or more embodiments described herein. Optimizable parameters may include, but are not limited to, the chemical classification system of the compound, the color code of the node, the cutoff value of the similarity score, the compound representation (name, chemical formula (dotted line), etc.), the number of nodes and generations by type-in ​​window, and / or the number of shared ions by type-in ​​window.

[0157] For example, general parameter 902 may include the number of connections to node 702U, 702K, or any node 702, the number of generations to visualize / display, etc.

[0158] The similarity score criterion parameter 904 may include a selection of the criteria on which the spectral similarity score 704 is based (e.g., cosine, HighChem, NIST, etc.).

[0159] The multi-class or hierarchical classification parameter 906 may include any appropriate hierarchical ranking or leveling, and / or any appropriate set of multi-class classifications suitable for multiple ontologities which may be chemical, classical, biological, functional, and / or toxicological. In one or more embodiments, two or more different hierarchical classification parameter categories may be used. For example, a set of multi-class chemical classifications may include, but is not limited to, drug abuse substances, natural compounds, surfactants, textile chemicals, extracts, elutes, marine toxins, personal care products, cosmetics, pharmaceuticals, and pesticides.

[0160] Visualization parameter 908 may include edge thickness, edge color, edge length, node color, node pattern, node border thickness, node border color, and / or node size.

[0161] The node visualization parameter 910 may include specifying the types and / or number of ions that should be included in the known analytical spectrum 634 used as the known node 702K. Note that the use of these categories is non-restrictive. Furthermore, these categories themselves are also non-restrictive.

[0162] Any combination of illustrated categories and / or parameters, and / or additional unillustrated categories and / or parameters, may be used by the generating component 618 and / or visualized on the interactive characteristics customization GUI 900 by the display component 620.

[0163] In one or more embodiments, these parameters shown in the interactive characteristic customization GUI 900 may be used, modified, adjusted and / or applied by the parameterization component 622 in combination with the generation component 618 and / or the display component 620. For example, the parameterization component 622 may apply a first characteristic of the spectral similarity score 704 as a first visual modification to the edges, and a second characteristic of the known analysis spectrum 634 as a second visual modification to the corresponding nodes of the known analysis spectrum 634. As another example, the parameterization component 622 may adjust at least one of the first or second visual modifications based on the selection of a characteristic class that includes characteristics other than at least one of the first or second characteristics on a graphical user interface including a visual display.

[0164] In other words, the operation of the parameterized component 622 may enable filtering of the spectral data that the generating component 618 uses to generate one or more spectral data groups 642.

[0165] Next, the execution component 624 can generally generate a response 692 to a query 630 (for example, if the query 630 includes a query). Such a query may include, but is not limited to, determining the classification, relationships, chemical family, closest spectrum, and identification of an unknown compound underlying the unknown spectrum 631. For example, the execution component 624 may identify the classification of the unknown spectrum 631 based on a visual display that includes a set of visual elements corresponding to the characteristics of the unknown spectrum 631 and the known analytical spectrum 634.

[0166] In summary, one or more embodiments described herein may provide comparison of an unknown spectrum 631 with an MN (e.g., representing a known spectrum 634) consisting of a highly curated spectral tree having different metadata classification schemes; simultaneous visualization of multiple nearest network families (e.g., multiple MN cloud visual representations 750) showing spectral relationships to a query spectrum; customizable visualization options (e.g., those shown in Figure 9); and / or support for a decision-making process to accurately determine the best hit (e.g., based on the operation of the execution component 624).

[0167] As a summary of the components and / or functions described above, we now refer to Figures 11 and 12, which show flowcharts of exemplary and non-limiting methods 1100 that can facilitate processes for generating, visualizing and / or utilizing molecular networks according to one or more embodiments described herein, such as the non-limiting system 600 of Figure 6. Although non-limiting methods 1100 are described in relation to the non-limiting system 600 of Figure 6, non-limiting methods 1100 may also be applied to other systems described herein, such as the non-limiting system 500 of Figure 5. For brevity, descriptions of similar elements and / or repetitions of processes used in each embodiment are omitted.

[0168] In 1102, the non-limiting method 1100 may include the system (e.g., acquisition component 610) acquiring an unknown spectral query (e.g., spectral query 630) for processing.

[0169] In 1104, a non-limiting method 1100 may include a system (e.g., evaluation component 612) performing a process of comparing first spectral data (e.g., unknown spectral data 632), which includes a first mass-to-charge ratio of an ion shown in an unknown spectrum (e.g., unknown spectrum 631), with second spectral data (e.g., known spectral data 638), which includes a second mass-to-charge ratio of an ion shown in a known analytical spectrum (e.g., known analytical spectrum 634), contained in a library of known analytical spectra (e.g., library data store 635).

[0170] In one or more embodiments, the comparison may be performed without additionally comparing the unknown spectrum with a second unknown spectrum.

[0171] In 1106, the non-limiting method 1100 may include, by means of a system (e.g., a scoring component 614), generating a spectral similarity score (e.g., a spectral similarity score 704) that shows a comparison between the mass-to-charge ratio of a first ion and the mass-to-charge ratio of a second ion.

[0172] In step 1108, the non-restrictive method 1100 may include determining by the system (e.g., the scoring component 614) whether there are additional known spectra in the library to compare with the unknown spectra. If yes, the non-restrictive method 1100 may return to step 1104 and continue processing. If no, the non-restrictive method may proceed to step 1110.

[0173] In 1110, a non-limiting method 1100 may apply an update to the library of known analysis spectra based on a comparison between the unknown spectrum and the known analysis spectrum, by the system (e.g., the update component 616).

[0174] In 1112, a non-limiting method 1100 may generate a cloud visualization (e.g., MN cloud visualization 750) by a system (e.g., a generating component 618). This cloud visualization may include a first-generation edge (e.g., edge 703) extending between an unknown spectrum and a first known group of analytical spectra in the library, and may include known analytical spectra. The edge may represent a first-generation spectral similarity score (e.g., spectral similarity score) between the unknown spectrum and the first group of analytical spectra.

[0175] In 1114, a non-limiting method 1100 may generate a second generation edge of the cloud visual representation by the system (e.g., a generating component 618). This second generation edge may extend between a first known analytical spectrum group (which may include known analytical spectra) and a second known analytical spectrum group in the library. The edge may represent a second generation spectral similarity score (which may include spectral similarity scores) between the first known analytical spectrum group and the second analytical spectrum group.

[0176] In 1116, a non-limiting method 1100 is to display a cloud visual representation in a graphical user interface (e.g., GUI 300) by the system (e.g., the display of component 620), consisting of ends corresponding to spectral similarity scores, between a pair of nodes (e.g., node 702), corresponding to unknown and known analysis regions.

[0177] In 1118, the non-limiting method 1100 may include the step of applying a first characteristic of the spectral similarity score (e.g., parameters 902-910) as a first visual modification (e.g., visual modification 690) of the edge by a system (e.g., parameterization component 622), and applying a second characteristic of the known analysis spectrum (e.g., parameters 902-910) as a second visual modification (e.g., visual modification 690) of each node of the known analysis spectrum.

[0178] In 1120, a non-limiting method 1100 may be used by a system (e.g., a parameterized component 622) to adjust at least one of the first visual modifications or the second visual modifications based on a selection in a graphical user interface including visuals, based on a class of characteristics that includes characteristics other than at least one of the first or second characteristics.

[0179] In 1122, the non-limiting method 1100 may involve a system (e.g., execution component 624) identifying a classification of the unknown spectrum (e.g., query response 692) based on a visual display including a set of visual elements corresponding to the characteristics of the unknown spectrum and the known analysis spectrum.

[0180] As a summary of the components and / or functions described above, we now refer to Figures 14 and 15, which show flowcharts of exemplary and non-limiting methods 1400 that can facilitate processes for generating, visualizing and / or utilizing molecular networks according to one or more embodiments described herein, such as the non-limiting system 600 of Figure 6. Although non-limiting methods 1400 are described in relation to the non-limiting system 600 of Figure 6, non-limiting methods 1400 may also be applied to other systems described herein, such as the non-limiting system 500 of Figure 5. For brevity, descriptions of similar elements and / or repetitions of processes used in each embodiment are omitted.

[0181] In 1402, the non-limiting method 1400 may include the system (e.g., acquisition component 610) acquiring an unknown spectral query (e.g., spectral query 630) for processing.

[0182] In 1404, the non-limiting method 1400 may include a system (e.g., evaluation component 612) performing a comparison between first spectral data (e.g., spectral data 632 or 638) and second spectral data (e.g., spectral data 632 or 638).

[0183] In one or more embodiments, the first spectral data may be unknown spectral data (e.g., unknown spectral data 632) that is not included in the molecular network (e.g., molecular network 640), and the second spectral data may be known spectral data (e.g., known spectral data 638) that is included in the molecular network.

[0184] In 1406, the non-limiting method 1400 may include, by a system (e.g., a scoring component 614), generating a spectral similarity score (e.g., a spectral similarity score 704) indicating the degree of similarity between the first spectral data and the second spectral data.

[0185] In 1408, a non-limiting method 1400 may include, by a system (e.g., a scoring component 614), generating a spectral similarity score that shows a comparison between a first mass-to-charge ratio of an ion in first spectral data and a second mass-to-charge ratio of an ion in second spectral data.

[0186] In step 1410, the non-restrictive method 1400 may include determining whether the system (e.g., the scoring component 614) has additional known spectra in the library to compare with the unknown spectra. If yes, the non-restrictive method 1400 may return to step 1404 and continue processing. If no, the non-restrictive method may proceed to step 1412.

[0187] In 1412, the non-limiting method 1400 may include, based on comparison, a system (e.g., an evaluation component 612) associating a first secondary characteristic (e.g., a secondary characteristic 645) corresponding to a first spectral data with a second spectral data, or a system (e.g., an evaluation component 612) associating a second secondary characteristic (e.g., a secondary characteristic 645) corresponding to a second spectral data with a first spectral data.

[0188] In one or more embodiments, one of the associated first secondary characteristics or second secondary characteristics may be defined by identification metadata (e.g., ID metadata 646) associated with the first or second spectral data.

[0189] Additionally, and / or alternatively, in one or more embodiments, one of the associated first secondary properties or second secondary properties may include, but are not limited to, one or more compound use classifications, substructure similarity, fragmentation kinetics degradation curves, optimal energy, number of peaks, chemical structure description classification or higher classification, toxicological properties, physicochemical properties, metabolic pathways, enzymatic reactions, biological reactions, enzymes or catalysts, or organisms or tissues.

[0190] In 1414, the non-limiting method 1400 may include, by a system (e.g., a generating component 618), generating a group of spectral data (e.g., a spectral data group 642) including first spectral data and second spectral data based on spectral similarity scores and associations.

[0191] In 1416, the non-limiting method 1400 may include, by a system (e.g., a generating component 618), generating a dataset or a grouping of spectral data containing data used to generate a visualization (e.g., a molecular network visual representation 643).

[0192] In 1418, a non-limiting method 1400 may include the system (e.g., display component 620) displaying a visualization on a graphical user interface (e.g., GUI 300) that includes edges (e.g., edge 703) corresponding to spectral similarity scores. The edges extend between a pair of nodes (e.g., node 702) corresponding to a first spectrum (e.g., spectrum 631 or 634) defined by first spectral data and a second spectrum (e.g., spectrum 631 or 634) defined by second spectral data.

[0193] In 1420, a non-limiting method 1400 may include, by a system (e.g., a parameterized component 622), applying a first characteristic of the spectral similarity score (e.g., parameters 902-910) as a first visual modification (e.g., visual modification 690) of an edge, and applying one of the associated first secondary characteristic or second secondary characteristic as a second visual modification (e.g., visual modification 690) of each node of the first or second spectrum.

[0194] In 1422, a non-limiting method 1400 may include, by a system (e.g., a parameterized component 622), adjusting at least one of a first visual modification or a second visual modification from a class of characteristics that includes characteristics other than at least one of the first or second characteristics, based on a selection in a graphical user interface including vision.

[0195] Additional Overview For the sake of brevity, the computerized and non-computerized implementations described herein are illustrated and / or described as a series of operations. It should be understood that the present invention is not limited to the actions and / or the order of actions shown. For example, actions may occur in one or more sequences and / or simultaneously, and may be performed in conjunction with other actions not described herein. Furthermore, not all actions shown are utilized in the implementation of computerized and non-computerized implementations according to the subject art described herein. In addition, computerized and non-computerized implementations may alternatively be represented as a series of interrelated states using state diagrams or events. Moreover, the computerized implementations described herein and throughout may be stored as products and used to transport and transfer computerized implementations to a computer. As used herein, the term “article of manufacture” is intended to include a computer program accessible from any computer-readable device or storage medium.

[0196] Systems and / or devices are described herein (and / or further described herein) in relation to the interaction between one or more components. Such systems and / or components may include the components or subcomponents described herein, one or more specified components and / or subcomponents, and / or additional components. Subcomponents may be implemented as components that are communicatively coupled to other components rather than being contained within a parent component. One or more components and / or subcomponents may be integrated into a single component that provides an aggregated function. Components may interact with one or more other components that are not specifically described herein for brevity but are well known to those skilled in the art.

[0197] In summary, one or more systems, computer program products and / or computer implementation methods described herein relate to a process for generating molecular networks. The system may include memories 504, 604 for storing computer executable components and processors 506, 606 for executing the computer executable components. The computer executable components may include evaluation components 512, 612 that perform a comparison between first spectral data 532, 632, which include first mass-to-charge ratios of ions shown in unknown spectra 531, 631, and second spectral data 538, 638, which include second mass-to-charge ratios of ions shown in known analytical spectra 534, 634 contained in known analytical spectrum libraries 535, 635, and update components 516, 616 that apply updates 542, 642 to the known analytical spectrum libraries 535, 635 based on a comparison between the unknown spectra 531, 631 and the known analytical spectra 534, 634.

[0198] In another summary, one or more systems, computer program products and / or computer implementation methods described herein relate to processes for the utilization of molecular networks. The system may comprise memories 504, 604 for storing computer executable components and processors 506, 606 for executing the computer executable components. The computer executable components include evaluation components 512, 612 for performing a comparison between first spectral data 532, 632 and second spectral data 532, 632; scoring components 514, 614 for generating spectral similarity scores 544, 704 indicating the degree of similarity between the first spectral data 532, 632 and the second spectral data 532, 632 based on the comparison; and first secondary characteristics 545, 645 corresponding to the first spectral data 532, 632 based on the comparison. The system may include parameterization components 522, 622 that associate spectral data 532, 632 with or associate second secondary characteristics 545, 645 corresponding to second spectral data 532, 632 with first spectral data 532, 632, and generation components 518, 618 that generate groupings of spectral data 542, 642, including first spectral data 532, 632 and second spectral data 532, 632, based on spectral similarity scores 544, 704 and associations.

[0199] One or more embodiments described herein utilize a novel system that provides limited error (e.g., a very small number of unknown data points to one) when updating a spectral library and generating molecular networks based thereon. In this way, it becomes possible to classify and / or identify unknown spectra by using known spectral data and progressively expanding the spectral data, while suppressing the accumulation of errors during generation (compared to existing frameworks that use multiple unknown data points when updating a spectral library for an unknown compound, for example).

[0200] Additionally and / or alternatively, one or more embodiments described herein can provide higher accuracy and / or more specific spectral data grouping using a novel system, thereby further suppressing unavailable spectral data returned based on queries and / or user-initiated and / or requested parameter adjustments and / or filtering adjustments. For example, in one or more embodiments described herein, spectral data grouping is generated from a molecular network and may be provided as one or more of the following: visual, data, metadata, etc. Spectral data grouping can be generated based on one or more of the following: a) one or more similarity scores between pairs of spectral data, or b) one or more secondary characteristics relating to at least one of the spectral data in a pair of spectral data. That is, in one or more embodiments, spectral data grouping can be performed based on both similarity scores and secondary characteristics. In one or more other embodiments, spectral data grouping can be performed based on the first secondary characteristic and at least one other secondary characteristic.

[0201] One or more embodiments described herein can be used to generate molecular networks that can provide diverse outputs during use of the molecular network. For example, based on visual elements in the form of an MN cloud, such as coloring, line thickness, shape, and / or distances between different elements within the MN cloud, a user subject or the system itself can predict one or more chemical properties and / or chemical relationships corresponding to an unknown spectrum. These one or more chemical properties and / or chemical relationships may include chemical classes, chemical uses, analogous compounds, and the like.

[0202] One or more embodiments described herein can provide a dynamically adjustable visual representation of a molecular network, enabling the provision of diverse visualization formats and / or customization of visualized chemical relationships and / or properties. For example, dynamic adjustability is found in the functionality of the generated molecular network (MN), allowing the user to interact with the visual representation to change chemical classes, chemical properties, the size and / or distance of each MN element, etc. Diverse visualizations may include large MN clouds, customized clouds based on one or more specified parameters, and configurations displaying multiple clouds simultaneously. Customization may be provided by using a graphical user interface (GUI) capable of representing different chemical properties and / or chemical relationships by nodes, edges, outlines of nodes and / or edges, fills of nodes and / or edges, line thickness within the cloud, distances between nodes, etc.

[0203] One or more embodiments described herein may be implemented within a scientific imaging device, in conjunction with such device, and / or connected to such device.

[0204] One or more embodiments disclosed herein can be applied in a plug-and-play manner to various architectures of existing spectral libraries and / or spectral data library data stores. That is, one or more embodiments described herein can generate molecular networks including visual representations of multiple chemical relationships, regardless of the data structure of the spectral library.

[0205] In fact, considering one or more embodiments described herein, a practical application of one or more systems, computer implementations, and / or computer program products described herein may provide grouping of spectral data based on combinations of secondary properties corresponding to spectral data and / or combinations of at least one secondary property and one or more similarity scores corresponding to spectral data. Spectral data grouping may be narrower, more specific, and / or more accurate based on such combinations than those that may be provided by existing frameworks. In connection with spectral data grouping, a molecular network visual representation may be realized and displayed. Spectral data grouping may enable an understanding of the chemical properties, relationships, and / or classification of unknown spectral queries in the data, metadata, and / or visual representation. That is, compared to existing frameworks that cannot provide this, one or more embodiments described herein may provide novel results that were previously unavailable, such as accurate spectral data grouping and / or molecular network (MN) updating. In one or more cases, this may be done without using multiple unknown data points that could improperly amplify errors associated with the generation and / or updating of molecular networks (MN).

[0206] These are useful and practical applications of computers, resulting in enhanced (e.g., improved and / or optimized) material analysis and image correction output. Overall, such computerized tools can constitute concrete and substantial technological improvements in the field of material analysis, more specifically in the field of material analysis using molecular networks, spectral data grouping, and / or the molecular network cloud visual representations generated therefrom.

[0207] Furthermore, one or more embodiments described herein may be used in actual systems based on the disclosed teachings. For example, one or more embodiments described herein may provide spectral data groupings generated based on one or more similarity scores and associated based on one or more secondary properties associated based on one or more similarity scores in the form of data, metadata and / or visualizations (e.g., graphic-based). Additionally, and / or alternatively, the process may be used to generate at least a portion of a molecular network by updating the molecular network (e.g., by updating the spectral library underlying the molecular network) based on at least the association of one or more secondary properties. These may be useful processes in various industries used in the fields of materials analysis, product manufacturing, quality control and / or similar fields. The embodiments disclosed herein may therefore provide improvements to scientific instrument technology (e.g., improvements to the computer technology supporting such scientific instruments, among other improvements).

[0208] In one or more cases, based on this, one or more molecular network cloud visual representations (and the data underlying the cloud visual representations) may be generated and analyzed, and as a result, one or more chemical correspondences (e.g., chemical properties, relationships, and / or classifications) may be determined for one or more unknown compound queries. These, too, can be useful processes in various industries used in fields such as materials analysis, product manufacturing, quality control, and / or similar fields.

[0209] Furthermore, one or more embodiments described herein can achieve operations of a predetermined scale. For example, two or more compound queries may be analyzed, and two or more corresponding spectral libraries may be updated based on them at least partially in parallel, while a separate process may be applied to a particular spectral query compared to another spectral query. In one or more cases, any combination of two or more spectral data groupings and / or MN cloud visual representations may be generated at least partially simultaneously.

[0210] Systems and / or devices are described herein (and / or further described herein) in relation to the interaction between one or more components. Such systems and / or components may include the components or subcomponents described herein, one or more specified components and / or subcomponents, and / or additional components. Subcomponents may be implemented as components that are communicatively coupled to other components rather than being contained within a parent component. One or more components and / or subcomponents may be integrated into a single component that provides an aggregated function. Components may interact with one or more other components that are not specifically described herein for brevity but are well known to those skilled in the art.

[0211] One or more embodiments described herein are, in one or more embodiments, inherently and / or inseparably linked to computer technology and cannot be implemented outside a computing environment. For example, one or more processes performed by one or more embodiments described herein may provide a more efficient and feasible execution of programs and / or program instructions for material analysis using molecular network generation and / or visualization compared to existing systems and / or technologies using molecular network generation and / or visualization. Systems, computer implementations and / or computer program products that provide the execution of these processes are extremely useful in the field of material analysis, for example, in determining one or more chemical correspondences (e.g., chemical properties, relationships and / or classifications) to one or more unknown compound queries, and are difficult to implement similarly practically outside a computing environment.

[0212] One or more embodiments described herein may utilize hardware and / or software to solve problems that are not highly technical and abstract and cannot be performed as a set of human mental actions. For example, even a human being or thousands of humans would find it difficult to efficiently, accurately, and / or effectively analyze computer data / metadata defining the spectra of multiple compounds using multiple chemical correspondences, and to generate, constrain, and / or adjust a digital display visual representation of a molecular network based on multiple spectral data, as one or more embodiments described herein may provide. Furthermore, neither the human mind nor a human being with a writing instrument and paper can perform one or more of these processes performed by one or more embodiments described herein.

[0213] In one or more embodiments, one or more of the processes described herein may be performed by one or more specialized computers (e.g., specialized processing units, specialized classical computers, specialized quantum computers, specialized hybrid classical / quantum systems, and / or other types of specialized computers) to perform defined tasks related to one or more of the above-described technologies. One or more embodiments and / or components thereof described herein may be used to solve new problems arising from the use of the above-described technological advancements, quantum computing systems, cloud computing systems, computer architectures, and / or other technologies.

[0214] One or more embodiments described herein are fully capable of performing one or more of the operations described herein while performing one or more other functions (e.g., fully powered on, fully running, and / or other functions).

[0215] As an additional summary, the embodiments and their features are listed below.

[0216] A system comprising a memory for storing computer executable components and a processor for executing computer executable components stored in the memory, wherein the computer executable components include an evaluation component that performs a process of comparing first spectral data, which includes a first mass-to-charge ratio of an ion shown in an unknown spectrum, with second spectral data, which includes a second mass-to-charge ratio of an ion shown in a known analytical spectrum in a known analytical spectrum library, and an update component that applies updates to the known analytical spectrum library based on the comparison between the unknown spectrum and the known analytical spectrum.

[0217] A system in which updates are applied without performing additional comparisons between the unknown spectrum and a second unknown spectrum.

[0218] A system as described in any of the preceding paragraphs, wherein a computer-executable component further includes a scoring component that generates a spectral similarity score showing a comparison between a first mass-to-charge ratio and a second mass-to-charge ratio of ions.

[0219] A system as described in any of the preceding paragraphs, wherein a computer executable component further includes a display component that displays a visual representation in a graphical user interface, including edges corresponding to spectral similarity scores extending between a pair of nodes corresponding to an unknown spectrum and a known analysis spectrum.

[0220] A system as described in any of the preceding paragraphs, wherein a computer executable component further generates a cloud visual representation including a first generation of edges extending between an unknown spectrum and a first set of known analyzed spectra in a library containing known analyzed spectra, the edges including a generating component representing a first generation spectral similarity score (including spectral similarity score) between the unknown spectrum and the first set of analyzed spectra.

[0221] In any of the systems of all paragraphs, the generating component further generates a second generation edge of the cloud visual representation, the edge extending between a first set of known analysis spectra containing known analysis spectra and a second set of known analysis spectra in the library, and the edge represents the second generation spectral similarity score (including spectral similarity score) between the first set of known analysis spectra and the second set of analysis spectra.

[0222] A system as described in any of the preceding paragraphs, further comprising a computer-executable component that displays a visual representation showing an unknown spectrum and a known analysis spectrum as a pair of nodes, with a spectral similarity score as the edge between them, and a parameterization component that applies a first property of the spectral similarity score as a first visual modification of the edge, and applies a second property of the known analysis spectrum as a second visual modification of each node of the known analysis spectrum.

[0223] In any of the systems described in the preceding paragraph, the parameterized component adjusts at least one of the first visual modification or the second visual modification based on a selection in a graphical user interface, including visuals, and based on a class of properties that includes properties other than at least one of the first or second properties.

[0224] A system as described in any of the preceding paragraphs, wherein a computer executable component further displays a visual representation that includes spectral similarity scores illustrated as edges between an unknown spectrum and a known analyzed spectrum, and that illustrates the unknown spectrum and the known analyzed spectrum as a pair of nodes, and the executable component further includes a display component that identifies the classification of the unknown spectrum based on the visual representation, which includes a set of visual elements corresponding to the characteristics of the unknown spectrum and the known analyzed spectrum.

[0225] A computer implementation method comprising, using a system operationally coupled with a processor, performing a comparison between first spectral data including a first mass-to-charge ratio of an ion shown in an unknown spectrum and second spectral data including a second mass-to-charge ratio of an ion shown in a known analytical spectrum library, and applying an update to the known analytical spectrum library based on the comparison.

[0226] Any computer implementation of any of the preceding paragraphs further includes the system applying the update without additionally comparing the unknown spectrum with a second unknown spectrum.

[0227] Any computer implementation of any of the preceding paragraphs further includes the system generating a spectral similarity score that shows a comparison between a first mass-to-charge ratio and a second mass-to-charge ratio of the ions.

[0228] Any computer implementation of any of the preceding paragraphs further includes the system displaying visual information in a graphical user interface, including edges corresponding to spectral similarity scores, wherein the edges extend between a pair of nodes corresponding to an unknown spectrum and a known analysis spectrum.

[0229] Any computer implementation of the preceding paragraph further includes the system generating a cloud visual representation, the cloud visual representation including a first-generation edge extending between an unknown spectrum and a first set of known analysis spectra in a library including known analysis spectra, the edge representing a first-generation spectral similarity score (including spectral similarity score) between the unknown spectrum and the first set of analysis spectra; and the system generating a second-generation edge of the cloud visual representation, the edge extending between a first set of known analysis spectra (including known analysis spectra) and a second set of known analysis spectra in a library, the edge representing a second-generation spectral similarity score (including spectral similarity score) between the first set of known analysis spectra and the second set of analysis spectra.

[0230] Any computer implementation of any of the preceding paragraphs further includes the system displaying visual information, the visual information including spectral similarity scores illustrated as edges between pairs of nodes representing unknown spectra and known analyzed spectra, and the system identifying the classification of unknown spectra based on the visual information, which includes a set of visual elements corresponding to the characteristics of unknown spectra and known analyzed spectra.

[0231] A computer program product that facilitates the process of updating a library of known analytical spectra with unknown spectra, comprising a computer-readable storage medium into which program instructions are incorporated, wherein the execution of the program instructions by a processor causes the processor to perform a comparison between first spectral data, which includes a first mass-to-charge ratio of ions shown in the unknown spectra, and second spectral data, which includes a second mass-to-charge ratio of ions shown in the known analytical spectra in the library of known analytical spectra, and applies updates to the library of known analytical spectra based on the comparison between the unknown spectra and the known analytical spectra.

[0232] In the computer program product described in the previous paragraph, program instructions are further executable by the processor, and it is possible to cause the processor to perform the operation of applying updates without performing additional comparisons between the unknown spectrum and a second unknown spectrum.

[0233] In any of the computer program products described in the preceding paragraph, program instructions are further executable by the processor, which can be instructed to generate a spectral similarity score that shows a comparison between a first mass-to-charge ratio and a second mass-to-charge ratio of ions.

[0234] In any of the computer program products described in the preceding paragraph, program instructions are further executable by the processor, which can cause the processor to display visual information in a graphical user interface, the visual information including edges corresponding to spectral similarity scores, extending between pairs of nodes corresponding to unknown spectra and known analysis spectra.

[0235] In any of the computer program products described in the preceding paragraph, program instructions are further executable by the processor, which displays visual information including spectral similarity scores illustrated as edges between pairs of nodes corresponding to unknown spectra and known analyzed spectra, and identifies the classification of unknown spectra based on a set of visual elements contained in the visual information that correspond to the characteristics of unknown spectra and known analyzed spectra.

[0236] A system comprising memory for storing computer executable components and a processor for executing computer executable components stored in memory, wherein the computer executable components include an evaluation component for performing a comparison between first spectral data and second spectral data; a scoring component for generating a spectral similarity score indicating the level of similarity between the first spectral data and the second spectral data based on the comparison; a parameterization component for associating a first secondary characteristic corresponding to the first spectral data with the second spectral data, or associating a second secondary characteristic corresponding to the second spectral data with the first spectral data based on the comparison; and a generation component for generating a grouping of spectral data including the first spectral data and the second spectral data based on the spectral similarity score and the association.

[0237] In the system described in the previous paragraph, the grouping of spectral data includes datasets or data used to generate visualizations.

[0238] In any of the systems described in the preceding paragraph, one of the associated first secondary characteristics or second secondary characteristics is defined by identification metadata associated with the first spectral data or the second spectral data.

[0239] In any of the systems described in the preceding paragraph, the scoring component generates a spectral similarity score that compares the first mass-to-charge ratio of an ion in the first spectral data with the second mass-to-charge ratio of an ion in the second spectral data.

[0240] In any of the systems described in the preceding paragraph, the first spectral data is unknown spectral data not included in the molecular network, and the second spectral data is known spectral data included in the molecular network.

[0241] In any of the systems described in the preceding paragraph, the first spectral data is the first unknown spectral data, and the second spectral data is the second unknown spectral data.

[0242] In any of the systems described in the preceding paragraph, one of the associated first secondary characteristics or second secondary characteristics includes one or more fragmentation dynamics, collision energy, neutral loss, or peak number.

[0243] In any of the systems described in the preceding paragraph, the associated first secondary property or second secondary property may include, but is not limited to, one or more compound use classifications, substructure similarity, fragmentation kinetics degradation curves, optimal energy, number of peaks, chemical structure description classification or higher classification, toxicological properties, physicochemical properties, metabolic pathways, enzymatic reactions, biological reactions, enzymes or catalysts, or organisms or tissues.

[0244] A system according to any of the preceding paragraphs, further comprising a computer executable component which displays a visual representation in a graphical user interface that includes edges corresponding to spectral similarity scores between pairs of nodes corresponding to a first spectrum defined by first spectral data and a second spectrum defined by second spectral data.

[0245] A system according to any of the preceding paragraphs, further comprising a parameterization component in which a computer-executable component applies a first characteristic of a spectral similarity score as a first visual modification of an edge, and for each of the nodes of the first spectrum or the second spectrum, applies either the associated first secondary characteristic or the second secondary characteristic as a second visual modification.

[0246] In any of the systems described in the preceding paragraph, the parameterized component adjusts at least one of a first visual change or a second visual change from a class of characteristics that includes characteristics other than at least one of the first or second characteristics, based on a selection in a graphical user interface, including visuals.

[0247] A computer implementation method comprising: performing a comparison between first spectral data and second spectral data using a system operationally connected to a processor; generating a spectral similarity score indicating the level of similarity between the first spectral data and the second spectral data based on the comparison; associating a first secondary characteristic corresponding to the first spectral data with the second spectral data, or associating a second secondary characteristic corresponding to the second spectral data with the first spectral data based on the comparison; and generating a grouping of spectral data including the first spectral data and the second spectral data based on the spectral similarity score and the association.

[0248] In the computer implementation described in the preceding paragraph, the grouping of spectral data includes a dataset or data used to generate visualizations.

[0249] In any of the computer implementations of the preceding paragraphs, one of the associated first secondary characteristics or second secondary characteristics is defined by identification metadata associated with the first spectral data or the second spectral data.

[0250] Any computer implementation of the preceding paragraph further includes the system generating a spectral similarity score that shows a comparison between a first mass-to-charge ratio of an ion in the first spectral data and a second mass-to-charge ratio of an ion in the second spectral data.

[0251] In any of the computer implementation methods described in the preceding paragraph, the first spectral data is unknown spectral data not included in the molecular network, and the second spectral data is known spectral data included in the molecular network.

[0252] In any of the computer implementation methods described in the preceding paragraph, the first spectral data is the first unknown spectral data, and the second spectral data is the second unknown spectral data.

[0253] In any of the computer implementations of the preceding paragraphs, one of the associated first secondary characteristics or second secondary characteristics includes one or more fragmentation dynamics, collision energy, neutral loss, or peak number.

[0254] In any of the computer-aided methods described in the preceding paragraph, one of the associated first secondary properties or second secondary properties includes, but is not limited to, one or more compound use classifications, substructure similarity, fragmentation kinetics degradation curves, optimal energy, number of peaks, chemical structure description classification or higher classification, toxicological properties, physicochemical properties, metabolic pathways, enzymatic reactions, biological reactions, enzymes or catalysts, or organisms or tissues.

[0255] A computer program product that facilitates the generation process of one or more spectral data groupings, comprising a computer-readable storage medium in which program instructions are embodied, wherein the program instructions are executed by a processor, and the processor is caused to perform the following actions: compare first spectral data and second spectral data; generate a spectral similarity score indicating the level of similarity between the first spectral data and the second spectral data based on the comparison; associate first secondary characteristics corresponding to the first spectral data with the second spectral data, or associate second secondary characteristics corresponding to the second spectral data with the first spectral data based on the comparison; and generate a spectral data grouping including the first spectral data and the second spectral data based on the spectral similarity score and the association.

[0256] In the computer program product described in the previous paragraph, the grouping of spectral data includes a dataset or data used to generate visualizations.

[0257] In any of the computer program products described in the preceding paragraph, one of the associated first secondary characteristics or second secondary characteristics is defined by identification metadata associated with the first spectral data or the second spectral data.

[0258] In any of the computer program products described in the preceding paragraph, the first spectral data is unknown spectral data not included in the molecular network, and the second spectral data is known spectral data included in the molecular network.

[0259] In any of the computer program products described in the preceding paragraph, the first spectral data is the first unknown spectral data, and the second spectral data is the second unknown spectral data.

[0260] In any of the computer program products described in the preceding paragraph, one of the associated first secondary characteristics or second secondary characteristics includes one or more fragmentation dynamics, collision energy, neutral loss, or peak number.

[0261] In any of the computer program products described in the preceding paragraph, one of the associated first secondary properties or second secondary properties includes, but is not limited to, one or more compound use classifications, substructure similarity, fragmentation kinetics degradation curves, optimal energy, number of peaks, chemical structure description classification or higher classification, toxicological properties, physicochemical properties, metabolic pathways, enzymatic reactions, biological reactions, enzymes or catalysts, or organisms or tissues.

[0262] Description of scientific instrument systems Next, with reference to Figure 16, a further detailed description of the context of one or more embodiments described in Figures 1 to 15 is given below. One or more computing devices implementing any scientific instrument module or method disclosed herein may be part of a scientific instrument system. Figure 16 shows a block diagram of an exemplary scientific instrument system 1600 in which one or more methods for scientific instruments disclosed herein or other methods may be performed according to various embodiments. Scientific instrument modules and methods disclosed herein (e.g., scientific instrument module 100 in Figure 1 and method 200 in Figure 2) may be implemented by one or more of the scientific instrument 1610, user-local computing device 1620, service-local computing device 1630, and / or remote computing device 1640 of the scientific instrument system 1600.

[0263] Any of the scientific instrument 1610, user local computing device 1620, service local computing device 1630, and / or remote computing device 1640 may include any embodiment of the computing device 400 described herein in relation to Figure 4. Alternatively, any of the scientific instrument 1610, user local computing device 1620, service local computing device 1630, and / or remote computing device 1640 may take one or more appropriate embodiments of the computing device 400 described herein in relation to Figure 4.

[0264] One or more of the scientific instrument 1610, the user local computing device 1620, the service local computing device 1630, and / or the remote computing device 1640 may include a processing device 1602, a storage device 1604, and / or an interface device 1606. The processing device 1602 can take any suitable form, including any form of the processor 402 described herein in relation to Figure 4. The processing devices 1602 included in each of the scientific instrument 1610, the user local computing device 1620, the service local computing device 1630, and / or the remote computing device 1640 may take the same form or different forms. The storage device 1604 can take any suitable form, and may include, for example, any form of the storage device 404 described herein in relation to Figure 4. The storage device 1604 is included in each of the scientific instrument 1610, the user's local computing device 1620, the service's local computing device 1630, and / or the remote computing device 1640, and they may take the same form or different forms. The interface device 1606 may take any suitable form, for example, any of the interface devices 406 described herein in relation to Figure 4. The interface devices 1606 included in each of the scientific instrument 1610, the user's local computing device 1620, the service's local computing device 1630, and / or the remote computing device 1640 may take the same form or different forms.

[0265] The scientific instrument 1610, the user local computing device 1620, the service local computing device 1630, and / or the remote computing device 1640 can communicate with other elements of the scientific instrument system 1600 via a communication path 1608. The communication path 1608 can connect the interface devices 1606 of each element of the scientific instrument system 1600 in a communicative manner, as shown in the figure. The communication path 1608 may also be a wired communication path or a wireless communication path (for example, it may follow any of the communication techniques described herein with respect to the interface device 406 of the computing device 400 in Figure 4), and may be either. The particular scientific instrument system 1600 shown in Figure 16 has a “fully connected” configuration with communication paths between each combination of the scientific instrument 1610, the user local computing device 1620, the service local computing device 1630, and the remote computing device 1640, but this is merely illustrative. In various embodiments, parts of the communication path 1608 may be omitted. For example, in one or more embodiments, the service local computing device 1630 may omit the direct communication path 1608 between its interface device 1606 and the interface device 1606 of the scientific instrument 1610, and instead communicate with the scientific instrument 1610 via the communication path 1608 between the service local computing device 1630 and the user local computing device 1620, or the communication path 1608 between the user local computing device 1620 and the scientific instrument 1610.

[0266] Scientific instrument 1610 may be any appropriate scientific instrument, such as a separation instrument, a mass spectrometer (MS) instrument, or other instrument that enables material analysis.

[0267] The user-local computing device 1620 may be a computing device local to the user of the scientific instrument 1610 (for example, in accordance with any embodiment of the computing device 400 described herein). In one or more embodiments, the user-local computing device 1620 may, but does not need to be, local to the scientific instrument 1610. For example, the user-local computing device 1620 associated with a user subject's home, office, or other building may be remote from the scientific instrument 1610, but may be able to communicate with the scientific instrument 1610, so that the user subject can use the user-local computing device 1620 to control the scientific instrument 1610 and / or access data from the scientific instrument 1610. In one or more embodiments, the user-local computing device 1620 may be a laptop, smartphone, or tablet device. In one or more embodiments, the user-local computing device 1620 may be a portable computing device. In one or more embodiments, the user-local computing device 1620 may be deployed in the field.

[0268] The service local computing device 1630 may be a computing device local to the entity that maintains the scientific instrument 1610 (for example, according to any embodiment of the computing device 400 discussed herein). For example, the service local computing device 1630 may be local to the manufacturer of the scientific instrument 1610, or local to a third-party service company. In one or more embodiments, the service local computing device 1630 may communicate with the scientific instrument 1610, the user local computing device 1620, and / or the remote computing device 1640 (for example, via a direct communication path 1608, or via a plurality of “indirect” communication paths 1608 as described above). This allows the service local computing device 1630 to receive data regarding the operation of the scientific instrument 1610, the user local computing device 1620, and / or the remote computing device 1640 (for example, self-diagnostic results of the scientific instrument 1610, calibration coefficients used by the scientific instrument 1610, measurements of sensors associated with the scientific instrument 1610, etc.). In one or more embodiments, the service local computing device 1630 may communicate with the scientific instrument 1610, the user local computing device 1620, and / or the remote computing device 1640 (for example, via a direct communication path 1608, or via a plurality of “indirect” communication paths 1608 as described above). This allows the service local computing device 1630 to transmit data to the scientific instrument 1610, the user local computing device 1620, and / or the remote computing device 1640 (for example, to update program instructions such as firmware in the scientific instrument 1610, to initiate the execution of a test or calibration sequence in the scientific instrument 1610, to update program instructions such as software in the user local computing device 1620 or the remote computing device 1640, etc.).A user of the scientific instrument 1610 may communicate with the service local computing device 1630 using the scientific instrument 1610 or the user local computing device 1620. This may be used to report problems with the scientific instrument 1610 or the user local computing device 1620, request a visit from a technician to improve the operation of the scientific instrument 1610, order consumables or replacement parts associated with the scientific instrument 1610, or for other purposes.

[0269] The remote computing device 1640 may be a computing device located remotely from the scientific instrument 1610 and / or the user local computing device 1620 (for example, according to any embodiment of the computing device 400 described herein). In one or more embodiments, the remote computing device 1640 may be included in a data center or other large-scale server environment. In one or more embodiments, the remote computing device 1640 may include network-attached storage (for example, as part of storage device 1604). The remote computing device 1640 may store data generated by the scientific instrument 1610 and perform analysis of the data generated by the scientific instrument 1610 (for example, according to program instructions). It may also facilitate communication between the user local computing device 1620 and the scientific instrument 1610, and / or communication between the service local computing device 1630 and the scientific instrument 1610.

[0270] In one or more embodiments, one or more elements of the scientific instrument system 1600 shown in Figure 16 may be omitted. Furthermore, in one or more embodiments, multiple elements of the scientific instrument system 1600 in Figure 16 may be present. For example, the scientific instrument system 1600 may include multiple user-local computing devices 1620 (e.g., multiple user-local computing devices 1620 associated with different user entities or located in different places). As another example, the scientific instrument system 1600 may include multiple scientific instruments 1610, each communicating with a service-local computing device 1630 and / or a remote computing device 1640. In such embodiments, the service-local computing device 1630 may monitor these multiple scientific instruments 1610, and the service-local computing device 1630 may simultaneously "broadcast" update information or other information to the multiple scientific instruments 1610. Multiple scientific instruments 1610 in the scientific instrument system 1600 may be located in close proximity to each other (e.g., in the same room) or in separate locations (e.g., on different floors, in different buildings, in different cities, etc.). In one or more embodiments, the scientific instruments 1610 may be connected to an Internet of Things (IoT) stack that enables command and control of the scientific instruments 1610 via web-based applications, virtual or augmented reality applications, mobile applications, and / or desktop applications. Any of these applications may be accessed by a user subject operating a user-local computing device 1620. In this case, the user-local computing device 1620 may communicate with the scientific instruments 1610 via a remote computing device 1640. In one or more embodiments, the scientific instruments 1610 may be sold by the manufacturer as part of a local scientific instrument computing unit 1612, together with one or more associated user-local computing devices 1620.

[0271] In one or more embodiments, the scientific instruments 1610 included in the scientific instrument system 1600 may be of different types. For example, one scientific instrument 1610 may be an EDS device, and other scientific instruments 1610 may be analytical devices that analyze the results of the EDS device. In some of these embodiments, a remote computing device 1640 and / or a user-local computing device 1620 may combine data from different types of scientific instruments 1610 included in the scientific instrument system 1600.

[0272] Exemplary operating environment Figure 17 is a schematic block diagram of an operating environment 1700 in which the described subjects may interact. The operating environment 1700 includes one or more remote components 1710. The remote components 1710 may be hardware and / or software (e.g., threads, processes, computing devices). In one or more embodiments, the remote components 1710 may be distributed computer systems and may be connected via a communication framework 1740 to programs that use local autoscaling components and / or resources of the distributed computer system. The communication framework 1740 may include wired network devices, wireless network devices, mobile devices, wearable devices, wireless access network devices, gateway devices, femtocell devices, servers, and the like.

[0273] The operating environment 1700 also includes one or more local components 1720. The local components 1720 may be hardware and / or software (e.g., threads, processes, computing devices). In one or more embodiments, the local components 1720 may include programs that communicate with or use auto-scaling components and / or remote resources 1710 and 1720, and may be connected to a remotely located distributed computing system via a communication framework 1740.

[0274] One possible form of communication between the remote component 1710 and the local component 1720 may be in the form of data packets coordinated for transmission between two or more computer processes. Another form of communication between the remote component 1710 and the local component 1720 may be in the form of circuit-switched data coordinated for transmission between two or more computer processes within a radio time slot. The operating environment 1700 includes a communication framework 1740 that can be used to facilitate communication between the remote component 1710 and the local component 1720, and this communication framework 1740 may include an air interface (e.g., an interface over a UMTS network, an interface over an LTE network, etc.). The remote component 1710 may be operably connected to one or more remote data stores 1750, such as a hard drive, solid-state drive, subscriber identification module (SIM) card, electronic SIM (eSIM), or device memory, which may be used to store information on the remote component 1710 side of the communication framework 1740. Similarly, the local component 1720 may be operablely connected to one or more local data stores 1730, which may be used to store information on the local component 1720 side of the communication framework 1740.

[0275] Exemplary computing environment To provide further context to each embodiment described herein, Figure 18 and the following description are intended to provide a brief and general description of a suitable computing environment 1800 in which each embodiment described herein may be implemented. Although these embodiments have been described above in the general context of computer execution instructions executable on one or more computers, those skilled in the art will understand that these embodiments may also be implemented in combination with other program modules or as a combination of hardware and software.

[0276] Generally, a program module includes routines, programs, components, data structures, etc., that perform tasks or implement abstract data types. Furthermore, the method can be implemented in other computer system configurations, such as single-processor or multi-processor computer systems, minicomputers, mainframe computers, Internet of Things (IoT) devices, distributed computing systems, personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, each of which may be operablely connected to one or more related devices.

[0277] The embodiments described herein can also be implemented in a distributed computing environment in which specific tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules may be located on both local and remote memory storage devices.

[0278] Computing devices typically include various media, which may include computer-readable storage media, machine-readable storage media, and / or communication media. In this specification, these two terms are used with distinct meanings, as will be discussed later. Computer-readable storage media or machine-readable storage media may be any available storage media accessible by a computer, including both volatile and non-volatile media, and removable and non-removable media. Exemplary and without limitation, computer-readable storage media or machine-readable storage media may be implemented in relation to any method or technique for storing information such as computer-readable or machine-readable instructions, program modules, structured data, or unstructured data.

[0279] Computer-readable storage media may include, but are not limited to, random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory and other memory technologies, compact disk read-only memory (CD-ROM), digital multipurpose disc (DVD), Blu-ray® disc (BD), or other optical disc storage, magnetic cassettes, magnetic tapes, magnetic disk storage devices and other magnetic storage devices, solid-state drives and other solid-state storage devices, or other tangible and / or non-temporary media that can be used to store desired information. In this regard, where the terms “tangible” or “non-temporary” are used herein to refer to storage, memory, or computer-readable media, these terms are excluded as modifiers of the propagating temporary signal itself, and do not waive any rights to standard storage, memory, or computer-readable media that are not the propagating temporary signal itself.

[0280] A computer-readable storage medium can be accessed by one or more local or remote computing devices. For example, it can be accessed for various processes related to the information stored on the medium via access requests, queries, and other data acquisition protocols.

[0281] A communication medium typically embodies computer-readable instructions, data structures, program modules, and other structured or unstructured data within a data signal such as a modulated data signal (e.g., a carrier wave or other transmission mechanism), and includes any information delivery or transport medium. The term "modulated data signal" or modulated signal refers to a signal in which one or more of its characteristics are set or changed to encode information in one or more signals. By way of example and not limitation, communication media include wired media such as wired networks and direct connections, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media.

[0282] Referring to FIG. 18, an example computing environment 1800 in which one or more embodiments described herein can be implemented includes a computer 1802, which includes a processing unit 1804, a system memory 1806, and a system bus 1808. The system bus 1808 couples system components including, but not limited to, the system memory 1806 to the processing unit 1804. The processing unit 1804 can be any of a variety of commercially available processors. Dual microprocessors and other multiprocessor architectures can also be used as the processing unit 1804.

[0283] The system bus 1808 may be any multiple types of bus structures that can be further interconnected to a memory bus (with or without a memory controller), a peripheral bus, and a local bus using any of the various commercially available bus architectures. The system memory 1806 includes ROM 1810 and RAM 1812. The basic input / output system (BIOS) may be stored in non-volatile memory such as ROM, erasable programmable read-only memory (EPROM), or electrically erasable programmable read-only memory (EEPROM), and the BIOS includes basic routines that help transfer information between elements within the computer 1802, such as during startup. RAM 1812 may include high-speed RAM, such as static RAM, for data caching.

[0284] Computer 1802 further includes an internal hard disk drive (HDD) 1814 (e.g., EIDE, SATA) and may include one or more external storage devices 1816 (e.g., a magnetic floppy disk drive (FDD) 1816, a memory stick or flash drive reader, a memory card reader, etc.). Although the internal HDD 1814 is shown as being located within computer 1802, the internal HDD 1814 may be housed in a suitable enclosure (not shown) and configured for external use. Furthermore, although not shown, in computing environment 1800, a solid-state drive (SSD) may be used in addition to or instead of the HDD 1814.

[0285] Other internal or external storage may include one or more other storage devices 1820 and storage media 1822 (e.g., solid state storage devices, non-volatile memory devices, and optical disk drives that are readable and writable from removable media (such as CD-ROM disks, DVDs, BDs, etc.)). External storage 1816 may be implemented by a network virtual machine. HDD 1814, external storage device 1816, and storage devices (e.g., drives) 1820 are each connected to system bus 1808 via HDD interface 1824, external storage interface 1826, and drive interface 1828, respectively.

[0286] These drives and the computer-readable storage media associated with them provide non-volatile storage of data, data structures, computer-executable instructions, etc. In computer 1802, these drives and storage media accommodate the storage of any data in an appropriate digital format. The above description of computer-readable storage media refers to various storage devices, but other types of storage media readable by a computer, whether existing or to be developed in the future, may also be used in the operating environment of this example. Further, such storage media can include computer-executable instructions for executing the methods described herein.

[0287] Multiple program modules may be stored in the drives and RAM 1812. This includes operating system 1830, one or more application programs 1832, other program modules 1834, and program data 1836. All or part of the operating system, applications, modules, and / or data may be cached in RAM 1812. The systems and methods described herein can be implemented using various commercially available operating systems or combinations of multiple operating systems.

[0288] Computer 1802 may optionally include emulation techniques. For example, a hypervisor (not shown) or other intermediary may emulate a hardware environment for operating system 1830, and the emulated hardware may optionally differ from the hardware shown in Figure 18. In such embodiments, operating system 1830 may include one VM from a plurality of virtual machines (VMs) hosted on computer 1802. Furthermore, operating system 1830 may provide a runtime environment for application 1832, such as a Java runtime environment or a .NET framework. The runtime environment is a consistent execution environment that allows application 1832 to run on any operating system that includes the runtime environment. Similarly, operating system 1830 may support containers, and application 1832 may be in the form of a container, which is a lightweight, standalone executable software package containing code, runtime, system tools, system libraries, application configuration, etc.

[0289] Furthermore, computer 1802 may include security modules such as a Trusted Processing Module (TPM). For example, with a TPM, the boot component calculates the hash of the next boot component in chronological order, waits for the result to match a secure value, and then loads the next boot component. This process can be performed at any layer of computer 1802's code execution stack (e.g., application execution level or operating system (OS) kernel level), thus enabling security at all levels of code execution.

[0290] A user subject may input commands and information to the computer 1802 via one or more wired / wireless input devices. Examples include a keyboard 1838, a touchscreen 1840, and a pointing device such as a mouse 1842. Other input devices (not shown) may include a microphone, an infrared (IR) remote control, a radio frequency (RF) remote control or other remote control, a joystick, a virtual reality controller and / or virtual reality headset, a gamepad, a stylus pen, an image input device (e.g., a camera), a gesture sensor input device, an eye-tracking sensor input device, an emotion or face detection device, a biometric input device (e.g., a fingerprint or iris scanner), and the like. These and other input devices are often connected to the processing unit 1804 via an input device interface 1844 that can be connected to the system bus 1808, but may also be connected via other interfaces such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, or a BLUETOOTH® interface.

[0291] Monitor 1846 or other types of display devices may also be connected to the system bus 1808 via an interface such as video adapter 1848. In addition to monitor 1846, the computer typically includes other peripheral output devices (not shown), such as speakers and printers.

[0292] Computer 1802 may operate in a network environment with one or more remote computers, such as remote computer 1850, using logical connections via wired and / or wireless communication. Remote computer 1850 may be a workstation, server computer, router, personal computer, portable computer, microprocessor-based entertainment device, peer device, or other common network node, typically including many or all of the elements described for computer 1802, but for brevity, only the memory / storage device 1852 is shown in this diagram. The illustrated logical connections are connected via wired / wireless connectivity to larger networks such as a local area network (LAN) 1854 and / or a wide area network (WAN) 1856. Such LAN and WAN network environments are common in offices and businesses, facilitating enterprise-wide computer networks such as intranets, all of which may be connected to global communication networks such as the Internet.

[0293] When used in a LAN network environment, computer 1802 may be connected to local network 1854 via a wired and / or wireless communication network interface or adapter 1858. Adapter 1858 may facilitate wired or wireless communication to LAN 1854. LAN 1854 may include a wireless access point (AP) for communicating with adapter 1858 in wireless mode.

[0294] When used in a WAN network environment, computer 1802 may include a modem 1860, or it may be connected to a communication server on WAN 1856 by other means, such as the Internet, and establish communication via WAN 1856. The modem 1860 may be an internal or external wired or wireless device and may be connected to the system bus 1808 via an input device interface 1844. In a network environment, program modules or parts thereof shown in relation to computer 1802 may be stored in a remote memory / storage device 1852. The illustrated network connection is an example, and other means may be used to establish communication links between computers.

[0295] When used in either a LAN or WAN network environment, computer 1802 may access a cloud storage system or other network-based storage system in addition to, or instead of, the aforementioned external storage device 1816. Generally, the connection between computer 1802 and the cloud storage system can be established via LAN 1854 or WAN 1856, for example, by adapter 1858 or modem 1860, respectively. When computer 1802 is connected to an associated cloud storage system, the external storage interface 1826 may, with the help of adapter 1858 and / or modem 1860, manage the storage provided by the cloud storage system in the same way as other types of external storage. For example, the external storage interface 1826 may be configured to provide access as if those cloud storage sources were physically connected to computer 1802.

[0296] Computer 1802 may be able to communicate with any wireless device or entity configured to operate wirelessly. Examples include printers, scanners, desktop and / or portable computers, personal digital assistants, communication satellites, any equipment or location associated with a wirelessly discoverable tag (e.g., kiosks, newspaper stands, store shelves, etc.), and telephones. This may include Wireless Fidelity (Wi-Fi) and Bluetooth® wireless technologies. Thus, communication may be a predefined structure in an existing network, or it may be simply ad-hoc communication between at least two devices.

[0297] Additional Information The embodiments described herein may be applied to one or more systems, methods, apparatus, and / or computer program products at any possible level of technical integration. A computer program product may include a computer-readable storage medium having computer-readable program instructions that cause a processor to operate in order to perform one or more aspects of the embodiments described herein. The computer-readable storage medium may be a tangible device capable of holding and storing instructions used by an instruction execution device. The computer-readable storage medium may, but are not limited to, electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, superconducting storage devices, and / or suitable combinations thereof. A non-exhaustive list of more specific examples of computer-readable storage media may include portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital utility disks (DVDs), memory sticks, floppy disks, devices on which instructions are recorded using mechanical encoding devices such as punch cards or grooved raised structures, and / or appropriate combinations thereof. Computer-readable storage media as used herein should not be interpreted as transient signals themselves, such as radio waves and / or other freely propagating electromagnetic waves, waveguides and / or other transmission media (e.g., optical pulses passing through fiber optic cables), and / or electrical signals transmitted through wires.

[0298] The computer-readable program instructions described herein may be downloaded to each computing / processing device from a computer-readable storage medium or to an external computer or external storage device via a network such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may include copper transmission cables, optical fibers, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface within each computing / processing device receives computer-readable program instructions from the network and transfers the computer-readable program instructions for storage in a computer-readable storage medium within each computing / processing device. The computer-readable program instructions for performing operations of one or more embodiments described herein may include assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, integrated circuit configuration data, and / or source code and / or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​(such as Smalltalk and C++) and procedural programming languages ​​("C" language and similar programming languages). Computer-readable program instructions may be executed entirely on a computer, partially on a computer and partially as a standalone software package, partially on a computer and partially on a remote computer, or entirely on a remote computer and / or server. In the latter scenario, the remote computer may be connected to the computer via any type of network, including a local area network (LAN) and / or a wide area network (WAN), and / or to an external computer, such as via the Internet using an Internet service provider.In one or more embodiments, electronic circuits including programmable logic circuits, field-programmable gate arrays (FPGAs), and / or programmable logic arrays (PLAs) may utilize state information of computer-readable program instructions to personalize the electronic circuits and execute computer-readable program instructions to perform aspects of one or more embodiments described herein.

[0299] Aspects of one or more embodiments described herein are described with reference to flowcharts and / or block diagrams, which represent methods, apparatus (systems), and computer program products according to one or more embodiments described herein. It is understood that each block in a flowchart and / or block diagram, and combinations of blocks in a flowchart and / or block diagram, can be implemented by computer-readable program instructions. These computer-readable program instructions may be provided to the processor of a general-purpose computer, a special-purpose computer, and / or other programmable data processing device. Thus, instructions executed via the processor of a computer or other programmable data processing device may generate means for performing the functions / operations specified in the blocks or combinations of blocks in a flowchart and / or block diagram. These computer-readable program instructions may be stored in a computer-readable storage medium capable of instructing a computer, a programmable data processing device, and / or other device to operate in a particular manner. In this way, the computer-readable storage medium may constitute an article of a product containing instructions for performing the aspects of the functions / operations specified in the blocks or combinations of blocks in a flowchart and / or block diagram. Computer-readable program instructions can be loaded into a computer, other programmable data processing devices, and / or other devices. This allows a series of actions to be performed on the computer, other programmable devices, and / or other devices, generating a computer-executed process, and the instructions executed on the computer, other programmable devices, and / or other devices implement the functions / actions specified by blocks or blocks in a flowchart and / or block diagram.

[0300] The flowcharts and block diagrams shown in the figures illustrate implementable configurations, functions, and / or operations of systems, computer implementable methods, and / or computer program products according to one or more embodiments described herein. In this regard, each block in a flowchart or block diagram may represent a module, segment, and / or part of an instruction containing one or more executable instructions for performing a specified logical function. In one or more alternative embodiments, the functions described in the blocks shown in the figures may be executed in an order different from the order shown. For example, two consecutively shown blocks may be executed substantially simultaneously, and / or the blocks may be executed in reverse order depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and / or combinations of blocks in a block diagram and / or flowchart, may be implemented by one or more combinations of purpose-specific hardware-based systems and / or purpose-specific hardware and / or computer instructions capable of performing a specified function and / or operation.

[0301] While this matter has been described above in the general context of computer execution instructions for computer program products running on a computer and / or multiple computers, those skilled in the art will recognize that one or more embodiments described herein may be implemented, at least in part, in parallel with one or more other program modules. Generally, a program module includes routines, programs, components, and / or data structures that perform a specific task and / or implement a specific abstract data type. Furthermore, the computer implementations described herein may also be implemented in other computer system configurations, including single-processor and / or multi-processor computer systems, minicomputing devices, mainframe computers, personal computers, handheld computing devices (e.g., PDAs, telephones), and / or microprocessor-based or programmable consumer and / or industrial electronic equipment. The illustrated aspects may also be implemented in a distributed computing environment in which tasks are performed by remote processing devices connected via a communication network. However, one or more aspects of one or more embodiments described herein may be implemented on a single computer. In a distributed computing environment, program modules may reside on both local and remote memory storage devices.

[0302] As used in this application, the terms “component,” “system,” “platform,” and / or “interface” refer to and / or may include computer-related entities or entities related to operating machines having one or more specific functions. Entities described herein may be hardware, a combination of hardware and software, software, or running software. For example, a component may be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program, and / or a computer. Exemplarily, both an application running on a server and the server itself may be components. One or more components may reside within a process and / or an execution thread, and components may be localized on one computer and / or distributed across two or more computers. As another example, each component may be executed from various computer-readable media storing various data structures. Components may communicate via local and / or remote processes. For example, they may communicate via signals having one or more data packets (e.g., data from one component to another interacting signally with other systems over a network such as a local system, a distributed system, and / or the Internet). As another example, a component may be a device having a specific function provided by mechanical parts driven by electrical or electronic circuits operated by software and / or firmware applications executed by a processor. In such a case, the processor may be inside or outside the device and may execute at least part of the software and / or firmware application. As yet another example, a component may be a device that provides a specific function via electronic components without mechanical parts, and the electronic components may include a processor and / or other means for executing software and / or firmware that at least partially grants the functionality of the electronic components.For example, the component may emulate an electronic component via a virtual machine, for instance, within a cloud computing system.

[0303] Furthermore, the term “or” shall mean an inclusive “or” rather than an exclusive “or.” That is, unless otherwise specified or it is clear from the context, “X uses A or B” shall mean any of the natural inclusive combinations. That is, “X uses A or B” shall be satisfied in any case whether X uses A, X uses B, or X uses both A and B. Furthermore, the articles “a” and “an” as used herein and in accompanying drawings shall generally be interpreted as meaning “one or more” unless otherwise specified or it is clear from the context that they are singular. Where used herein, the terms “example” and / or “exemplary” mean serving as an example, case, or illustration. To avoid misunderstanding, the subject matter described herein is not limited to such examples. Furthermore, any aspect or design described herein as “example” and / or “exemplary” shall not necessarily be construed as being preferable or more advantageous than other aspects or designs, nor shall it preclude equivalent exemplary structures and techniques known to those skilled in the art.

[0304] As used herein, the term “processor” may refer to substantially any computing processing unit and / or device, including but not limited to single-core processors, single processors with software multithreading capabilities, multi-core processors, multi-core processors with software multithreading capabilities, multi-core processors with hardware multithreading technology, parallel platforms, and / or parallel platforms with distributed shared memory. Furthermore, a processor may refer to integrated circuits, application-specific integrated circuits (ASICs), digital signal processors (DSPs), field-programmable gate arrays (FPGAs), programmable logic controllers (PLCs), complex programmable logic devices (CPLDs), discrete gate or transistor logic, discrete hardware components, and / or any combination thereof designed to perform the functions described herein. Furthermore, a processor may leverage technologies, including but not limited to nanoscale architectures such as molecular and quantum dot-based transistors, switches, and / or gates, for optimizing space utilization and / or improving the performance of associated equipment. A processor may be implemented as a combination of multiple computing processing units.

[0305] As used herein, terms related to "memory", "storage", "data store", "data storage", "database", and substantially all other information storage components related to the operation and function of components refer to "memory components", "entities embodied in memory", or components including memory. The memory and / or memory components described herein may be volatile memory or non-volatile memory, or may include both volatile memory and non-volatile memory. By way of example and without limitation, non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM), flash memory, and / or non-volatile random access memory (RAM) (such as ferroelectric RAM (FeRAM)). Volatile memory may include, for example, RAM that functions as an external cache memory. By way of example and without limitation, RAM may be provided in many forms such as synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), extended SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), direct rambus RAM (DRRAM), direct rambus dynamic RAM (DRDRAM), and / or rambus dynamic RAM (RDRAM). Further, the memory components of the systems and / or computer-implemented methods described herein are intended to include these and / or other suitable types of memory without being limited thereto.

[0306] The above description includes only examples of systems and computer implementations. Of course, it is impossible to describe every conceivable combination of components and / or computer implementations for the purpose of illustrating one or more embodiments, but those skilled in the art will recognize that many more combinations and / or permutations of one or more embodiments are possible. Furthermore, where terms such as “include,” “have,” and “equip” are used in the descriptions, claims, appendices, and / or drawings for carrying out the invention, these terms are intended to be interpreted comprehensively, similar to “comprising” as used as a transitional term in the claims.

[0307] In the descriptions of various embodiments, expressions such as "one embodiment," "various embodiments," "one or more embodiments," and / or "several embodiments" may be used, and these may all refer to one or more embodiments, whether identical or distinct.

[0308] The various embodiments are described for illustrative purposes only and are not intended to be exhaustive or limitful to the embodiments described herein. Many modifications and variations will be obvious to those skilled in the art and can be made without departing from the scope and spirit of the embodiments described herein. The terminology used herein has been selected to best describe the principles, practical applications and / or technical improvements to existing technologies of the embodiments and / or to enable those skilled in the art to understand the embodiments described herein.

Claims

1. It is a system, Memory that stores computer executable components, The system comprises a processor that executes the computer executable component stored in the memory, wherein the computer executable component is An evaluation component that performs a comparison between the first spectral data and the second spectral data, A scoring component that generates a spectral similarity score indicating the level of similarity between the first spectral data and the second spectral data based on the comparison, A parameterization component that, based on the comparison, associates a first secondary characteristic corresponding to the first spectral data with the second spectral data, or associates a second secondary characteristic corresponding to the second spectral data with the first spectral data, A system comprising: a generating component that generates a grouping of spectral data, including the first spectral data and the second spectral data, based on the spectral similarity score and the association.

2. The system according to claim 1, wherein the grouping of spectral data includes a dataset or data used to generate visualizations.

3. The system according to claim 1, wherein one of the associated first secondary characteristics or the second secondary characteristics is defined by identification metadata associated with the first spectral data or the second spectral data.

4. The system according to claim 1, wherein the scoring component generates a spectral similarity score that shows a comparison between a first mass-to-charge ratio of an ion in the first spectral data and a second mass-to-charge ratio of an ion in the second spectral data.

5. The system according to claim 1, wherein the first spectral data is a first unknown spectral data, and the second spectral data is a second unknown spectral data.

6. The system according to claim 1, wherein one of the associated first secondary properties or the second secondary property includes, but is not limited to, one or more compound usage classifications, substructure similarity, fragmentation kinetics degradation curves, optimal energy, number of peaks, chemical structure description classification or higher classification, toxicological properties, physicochemical properties, metabolic pathways, enzymatic reactions, biological reactions, enzymes or catalysts, or organisms or tissues.

7. The aforementioned computer executable component, The system according to claim 1, further comprising a display component that displays a visual representation in a graphical user interface, including edges corresponding to the spectral similarity score, extending between pairs of nodes corresponding to a first spectrum defined by the first spectral data and a second spectrum defined by the second spectral data.

8. The aforementioned computer executable component, The system according to claim 7, further comprising a parameterization component for applying a first characteristic of the spectral similarity score as a first visual modification of the edge, and applying either the associated first secondary characteristic or the second secondary characteristic as a second visual modification of the respective nodes of the first spectrum or the second spectrum.

9. The system according to claim 8, wherein the parameterized component adjusts at least one of the first visual modification or the second visual modification from a class of characteristics that includes characteristics other than at least one of the first characteristics or the second characteristics, based on a selection in a graphical user interface including the visual display.

10. A computer implementation method, A system operably connected to the processor performs a comparison between the first spectral data and the second spectral data, Based on the above comparison, the system generates a spectral similarity score indicating the level of similarity between the first spectral data and the second spectral data. Based on the above comparison, the system may associate a first secondary characteristic corresponding to the first spectral data with the second spectral data, or the system may associate a second secondary characteristic corresponding to the second spectral data with the first spectral data. A computer implementation method comprising: generating a group of spectral data including the first spectral data and the second spectral data based on the spectral similarity score and the association using the system.

11. The computer implementation method according to claim 10, wherein the grouping of spectral data includes a dataset or data used to generate visualizations.

12. The computer implementation method according to claim 10, wherein one of the associated first secondary characteristics or the second secondary characteristics is defined by identification metadata associated with the first spectral data or the second spectral data.

13. The computer implementation method according to claim 10, further comprising generating a spectral similarity score by the system that shows a comparison between a first mass-to-charge ratio of an ion in the first spectral data and a second mass-to-charge ratio of an ion in the second spectral data.

14. The computer implementation method according to claim 10, wherein the first spectral data is a first unknown spectral data, and the second spectral data is a second unknown spectral data.

15. The computer-aided method according to claim 10, wherein one of the associated first secondary properties or the second secondary property includes, but is not limited to, one or more compound usage classifications, substructure similarity, fragmentation kinetics degradation curves, optimal energy, number of peaks, chemical structure description classification or higher classification, toxicological properties, physicochemical properties, metabolic pathways, enzymatic reactions, biological reactions, enzymes or catalysts, or organisms or tissues.

16. A computer program product that facilitates the generation process of one or more spectral data groupings, comprising a computer-readable storage medium in which program instructions are embodied, wherein the program instructions are executed by a processor, and the processor, The processor is used to perform a comparison between the first spectral data and the second spectral data. Based on the above comparison, the processor generates a spectral similarity score indicating the level of similarity between the first spectral data and the second spectral data. Based on the comparison, the processor associates the first secondary characteristic corresponding to the first spectral data with the second spectral data, or the processor associates the second secondary characteristic corresponding to the second spectral data with the first spectral data. A computer program product that causes the processor to generate a grouping of spectral data, including the first spectral data and the second spectral data, based on the spectral similarity score and the association.

17. The computer program product according to claim 16, wherein the grouping of spectral data includes a dataset or data used to generate visualizations.

18. The computer program product according to claim 16, wherein one of the associated first secondary characteristics or the second secondary characteristics is defined by identification metadata associated with the first spectral data or the second spectral data.

19. The computer program product according to claim 16, wherein the first spectral data is a first unknown spectral data, and the second spectral data is a second unknown spectral data.

20. The computer program product according to claim 16, wherein one of the associated first secondary properties or the second secondary property includes, but is not limited to, one or more compound usage classifications, substructure similarity, fragmentation kinetics degradation curves, optimal energy, number of peaks, chemical structure description classification or higher classification, toxicological properties, physicochemical properties, metabolic pathways, enzymatic reactions, biological reactions, enzymes or catalysts, or organisms or tissues.

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