A method, device, medium and computer for acquiring exhaled metabolite data

Abstract

The present invention provides a method, device, medium and computer for acquiring breath metabolite data. The method relates to the technical field of digital data processing, and comprises: sending a first resource locator to a database for retrieval, receiving a first retrieval result; parsing the first retrieval result to obtain a document identifier; sending a second resource locator to a predetermined life science literature database for retrieval, receiving a second retrieval result returned by the life science literature database; parsing the second retrieval result to obtain document data corresponding to the document identifier one by one; screening the document data to generate structured breath metabolite data; adopting the method of the present invention, it is possible to acquire document data related to breath metabolites from an existing life science literature database, and by structuring the data and arranging the data into a standardized format, it is possible to provide high-quality data to be entered for the breath metabolite database.

Description

Technical Field

[0001] The present invention relates to the technical field of digital data processing, and more specifically, to a method, device, medium and computer for acquiring breath metabolite data. Background Art

[0002] Human exhaled breath analysis is widely used in life and clinical practice. By analyzing the concentration of specific substances in exhaled breath, we can understand the physiological and pathological state of the human body. Common breath analysis equipment includes handheld alcohol testers and end-tidal carbon dioxide monitors used in operating rooms. These devices detect specific exhaled metabolites and quickly provide concentration information of the compounds. The prerequisite for developing exhaled breath analysis equipment is to determine the compounds that need to be detected. To achieve this goal, researchers need to conduct exhaled metabolomics research to explore exhaled biomarkers related to the external phenotypes of the human body (such as disease, age, diet, etc.).

[0003] like Figure 4 As shown in the figure, breath metabolomics research can be summarized into the following eight core steps: (1) raise scientific questions from a clinical or biological perspective; (2) formulate an experimental plan based on the research purpose, which should take into account the characteristics of the sample population and select appropriate analytical instruments; (3) collect breath data while implementing quality control measures and store the experimental data; (4) obtain the breath feature intensity matrix of each sample through breath data preprocessing; (5) perform statistical analysis to screen out breath features with differential or diagnostic effects; (6) identify breath features as potential biomarkers by matching reference spectral databases, detecting standards, etc.; (7) perform metabolomics analysis based on the identified compounds to clarify the biological functions of metabolites; (8) conduct validation studies and use independent research cohorts to verify the effectiveness of potential biomarkers.

[0004] In the research stages of experimental design, compound annotation, and biological significance, researchers often need to spend a lot of time searching the literature and extracting information related to exhaled metabolites. For example, reported exhaled metabolites, human diseases related to metabolites, potential exhaled biomarkers for various diseases, and diagnostic effects, etc. Therefore, establishing a dedicated exhaled metabolite database can reduce the time wasted on retrieving duplicate information, improve research efficiency, and disseminate research results in a timely manner.

[0005] The Human Metabolome Database (HMDB) is the most widely used authoritative database in the field of human metabolomics research. It contains more than 200,000 human metabolites, as well as the corresponding compound descriptions, names, synonyms, chemical structures, nuclear magnetic resonance spectra, mass spectra, concentrations in biological samples, related diseases, metabolic pathways, references and other information. Among these metabolites, only 60 are labeled as exhaled metabolites, while researchers have currently detected more than 1,500 compounds in exhaled breath. This huge quantitative gap means that HMDB's information coverage of exhaled metabolites is far less than the research progress of exhaled metabolomics, and cannot meet the research needs of exhaled metabolomics. Therefore, researchers need to independently obtain information related to exhaled metabolites.

[0006] In order to solve the above problems, it is necessary to propose a method that can efficiently obtain exhaled metabolite data. Summary of the invention

[0007] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a method, device, medium and computer for acquiring breath metabolite data, so as to overcome the shortcomings of the prior art database, such as poor data timeliness and small amount of information.

[0008] The above technical objectives of the present invention are achieved through the following technical solutions:

[0009] In a first aspect, a method for acquiring exhaled metabolite data comprises:

[0010] S1. Construct search statements related to breath metabolites;

[0011] S2, converting a first resource locator for document retrieval based on the search statement, sending the first resource locator to a predetermined life science document database for retrieval, and receiving a first search result returned by the life science document database; parsing the first search result to obtain all document identifiers associated with the search statement;

[0012] S3, based on each of the document identifiers, converting them into corresponding second resource locators, sending the second resource locators to a predetermined life science document database for retrieval, receiving second retrieval results returned by the life science document database; parsing the second retrieval results to obtain document data corresponding to the document identifiers one by one;

[0013] S4. Filter the literature data based on predetermined filtering conditions; and generate structured breath metabolite data using the literature data that meets the filtering conditions.

[0014] In one embodiment, the life science literature database is specifically the PubMed database.

[0015] In one embodiment, parsing the first search result to obtain all document identifiers associated with the search statement specifically includes: converting the first search result from a byte stream format into a tree structure; and determining each document identifier based on the converted first search result.

[0016] In one embodiment, converting the first search result from a byte stream format into a tree structure specifically includes: based on a predetermined encoding format, decoding the first search result in a byte stream format to generate a first search result in a string format; identifying tags in the first search result in a string format, and based on the tags, constructing a nested relationship of the first search result; based on the nested relationship, converting the first search result into a first search result in a tree structure.

[0017] In one embodiment, the identification of tags in the first search result in string format and the construction of a nested relationship of the first search result based on the tags specifically include: identifying all start tags and all end tags in the first search result; matching each start tag and each end tag one by one with each other; determining whether the start tag A is after the start tag B and whether the end tag A is before the end tag B, and if so, determining that tag A is nested inside tag B.

[0018] In one embodiment, determining each document identifier based on the converted first search result specifically includes: identifying all ID sub-nodes nested in the IDLIST node in the first search result of the tree structure; and obtaining the document identifier contained in each ID sub-node.

[0019] In one embodiment, parsing the second search result to obtain document data corresponding to the document identifier specifically includes: converting the second search result from a byte stream format into a tree structure; identifying an articletitle node, an abstracttext node, and an articleid[@idtype='doi'] node in the second search result of the tree structure; extracting the corresponding document title based on the articletitle node; extracting the corresponding document abstract based on the abstracttext node; and extracting the corresponding unique identifier based on the articleid[@idtype='doi'] node.

[0020] In one embodiment, it also includes: after obtaining the second search result and generating structured breath metabolite data, using the second resource locator as a key and the corresponding structured breath metabolite data as a value, storing them in the cache mechanism in the form of a key-value pair; when the data generation method is interrupted and needs to be continued, performing the following operations to determine whether the same second resource locator exists in the cache mechanism: traversing the keys in the cache mechanism, and performing string matching comparisons on the second resource locator to be determined and the keys in the cache one by one; if a completely identical key is found during the traversal process, it is determined that the same second resource locator is stored in the cache mechanism; if the same key is not found after traversing the entire cache mechanism, it is determined that the same second resource locator does not exist in the cache mechanism, and then the second resource locator is sent to a predetermined life science literature database for retrieval.

[0021] In one embodiment, the search statement includes: a first keyword for limiting the research object, a second keyword for limiting the detection method, a third keyword for limiting the detection sample type, and a fourth keyword for limiting the target disease or physiological state.

[0022] In one embodiment, only the fourth keyword is different between the search statements.

[0023] In one embodiment, the screening of the literature data based on the predetermined screening conditions specifically includes: determining whether the literature data includes the following four types of information: the name of the human disease studied, the differential breath metabolites associated with the disease, the variation characteristics of the differential breath metabolites in the experimental group, and the type of breath sample;

[0024] If the document data lacks any of the above information, it is determined that the document data does not meet the screening conditions.

[0025] In one embodiment, the use of literature data that meets the screening conditions to generate structured breath metabolite data specifically includes: based on the screened literature data, obtaining the name of the human disease being studied, the differential breath metabolites associated with the disease, the change characteristics of the differential breath metabolites in the experimental group, the type of breath sample, and the title corresponding to the literature data, and constructing a breath metabolite data table.

[0026] In one embodiment, the construction of the breath metabolite data table specifically includes: converting the human disease name into the corresponding disease preferred name in the MeSH database and the corresponding MeSH ID; converting the differential breath metabolites into the preferred name of the corresponding compound in PubChem and the corresponding InChIKey; the change characteristics specifically include: increase, decrease and no significant change; the type of the breath sample specifically includes: exhaled gas or exhaled condensate; converting the title into a literature title and the corresponding PubMed ID.

[0027] In a second aspect, a device for acquiring exhaled metabolite data comprises:

[0028] A building block for constructing search statements related to breath metabolites;

[0029] A first search unit is used to convert a first resource locator for document retrieval based on the search statement, send the first resource locator to a predetermined life science document database for retrieval, receive a first search result returned by the life science document database; parse the first search result to obtain all document identifiers associated with the search statement;

[0030] A second search unit is used to convert each of the document identifiers into a corresponding second resource locator, send the second resource locator to a predetermined life science document database for search, receive a second search result returned by the life science document database; parse the second search result to obtain document data corresponding to the document identifiers one by one;

[0031] The generating unit is used to screen the literature data based on a predetermined screening condition; and generate structured breath metabolite data using the literature data that meets the screening condition.

[0032] In a third aspect, a computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the steps of any one of the methods in the first aspect.

[0033] In a fourth aspect, a computer comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the methods in the first aspect when executing the computer program.

[0034] In summary, the present invention has the following beneficial effects: a method for acquiring breath metabolite data, comprising: constructing a search statement associated with breath metabolites; writing a first resource locator for literature retrieval based on the search statement, and sending the first resource locator to a predetermined life science literature database for retrieval; receiving a first retrieval result returned by the life science literature database; writing a corresponding second resource locator based on each of the document identifiers, and sending the second resource locator to a predetermined life science literature database for retrieval; receiving a second retrieval result returned by the life science literature database; filtering the literature data based on predetermined filtering conditions; generating structured breath metabolite data using the literature data that meets the filtering conditions; using the method of the present invention, literature data related to breath metabolites can be obtained from an existing life science literature database, and by filtering the literature data, it can be ensured that the filtered data meets the needs of the breath metabolite database, and by structuring the data and organizing the data into a standardized format, it can provide high-quality data to be entered for the breath metabolite database. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A flow chart of a method for acquiring breath metabolite data of the present invention;

[0036] Figure 2 is a structural diagram of a breath metabolite data acquisition device in an embodiment of the present invention;

[0037] Figure 3 is an internal structure diagram of a computer in an embodiment of the present invention;

[0038] Figure 4 A flow chart of the steps of breath metabolomics research in the prior art;

[0039] Figure 5 This is a schematic diagram of the first search result of the present invention;

[0040] Figure 6 is a schematic diagram of the second search result of the present invention;

[0041] Figure 7 For the structure of the present invention

[0042] In the figure: 1. Construction unit; 2. First retrieval unit; 3. Second retrieval unit; 4. Generation unit. DETAILED DESCRIPTION

[0043] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings. Several embodiments of the present invention are shown in the accompanying drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein.

[0044] In the embodiments of the present application, "at least one" refers to one or more, and "plurality" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and subsequent associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0045] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0046] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0047] In several embodiments provided in the present application, any function can be stored in a computer-readable storage medium if it is implemented in the form of a software functional unit and sold or used as an independent product. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for enabling a device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0048] The above is only a specific implementation of the present application. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. The protection scope of the present application should be based on the protection scope of the claims.

[0049] The present invention is described in detail below in conjunction with the accompanying drawings and embodiments.

[0050] Embodiment 1

[0051] In order to solve the above problems, the present invention provides a method for acquiring breath metabolite data, such as Figure 1 As shown, including:

[0052] S1. Construct search statements related to breath metabolites;

[0053] S2, converting a first resource locator for document retrieval based on the search statement, sending the first resource locator to a predetermined life science document database for retrieval, and receiving a first search result returned by the life science document database; parsing the first search result to obtain all document identifiers associated with the search statement;

[0054] S3, based on each of the document identifiers, converting them into corresponding second resource locators, sending the second resource locators to a predetermined life science document database for retrieval, receiving second retrieval results returned by the life science document database; parsing the second retrieval results to obtain document data corresponding to the document identifiers one by one;

[0055] S4. Filter the literature data based on predetermined filtering conditions; and generate structured breath metabolite data using the literature data that meets the filtering conditions.

[0056] In practical applications, search statements are used to limit the search target. Since the target literature database is a life science database, it not only contains literature data on breath metabolites, but also covers published literature data in the fields of medicine, pharmacy, molecular biology, etc. Therefore, how to retrieve and screen literature related to breath metabolites from life science databases, and extract formatted data based on the literature that can be used to build a breath metabolite database, is an urgent problem to be solved.

[0057] In order to retrieve data related to exhaled breath metabolites from a life science database, it is first necessary to construct a search statement based on the search target. The search statement is based on one or more keywords and is formed by associating multiple keywords using logical operators. Each keyword is used to limit the search scope or remove noise in the search results to make the search results more accurate. In this application, the keywords in the search statement are used to retrieve literature data related to exhaled breath metabolites from a life science database.

[0058] The search statement is converted into a first resource locator that meets the database requirements and sent to the database; after the database receives the first resource locator, it can return a search result to the user, which contains all document identifiers that meet the search statement. After parsing the search results, the unique identifier of each document can be obtained. In the target database, the document identifier and the document are in a one-to-one correspondence, that is, the document identifier can be used to find the unique corresponding document data in the database.

[0059] Since the search results obtained by the first resource locator only include document identifiers, but do not include detailed data related to the document content, after identifying and extracting each document identifier in the first search result, it is necessary to construct a corresponding second resource locator for each identifier and send them one by one to the life science database for further search to obtain the document data corresponding to each document identifier.

[0060] After the above steps, all the literature data associated with the search statement in the life science database can be obtained. Since the search results cannot be guaranteed to be 100% accurate, it is necessary to screen the received literature data and remove the literature data that is not related to the exhaled metabolites, and finally obtain the literature data that has a strong correlation with the human exhaled metabolomics. In order to convert the literature data into basic data for building a metabolomics database, it is also necessary to extract information related to the exhaled metabolites from the literature data, and structure this information to finally generate the exhaled metabolite data for building the exhaled metabolite database.

[0061] In summary, by adopting the method of the present invention, literature data related to breath metabolites can be obtained from the existing life science literature database, and by screening the literature data, it can be ensured that the screened data meets the needs of the breath metabolite database, and by structuring the data and organizing the data into a standardized format, high-quality data to be entered can be provided for the breath metabolite database.

[0062] In one embodiment, the life science literature database is specifically the PubMed database.

[0063] In practical applications, PubMed is the world's largest biomedical and life science literature database, currently managed and maintained by the National Center for Biotechnology Information (NCBI) of the United States. Due to the advantages of PubMed such as comprehensive literature, free and open access, and rich biomedical cross-reference information, this application selects PubMed as the literature source database.

[0064] In one embodiment, the writing of the first resource locator for document retrieval based on the search statement specifically includes: converting each symbol in the search statement into a URL, filling the search statement containing the URL into a predetermined template, and generating a first resource locator.

[0065] In this embodiment, the search statement first needs to be converted into a statement in URL format that can be recognized and accepted by the PubMed database. The conversion process specifically includes: encoding some special characters into URL, for example, encoding a space into %20, encoding a double quote into %22, encoding # into %23, etc. After the conversion is completed, the search statement in URL format is inserted after the beginning of the string specified by the PubMed database, and the concatenated string is sent to the PubMed database as the first resource locator for retrieval.

[0066] In this embodiment, when the user wants to search for documents containing the keyword "metabolomics database" in the title, "metabolomics database [Title]" can be used as a search statement to construct a corresponding first resource locator in URL format.

[0067] After sending the above resource locator, the database will return the search results to the client, such as Figure 5 As shown, it includes 6 document IDs that meet the search formula requirements, that is, document identifiers.

[0068] In one embodiment, parsing the first search result to obtain all document identifiers associated with the search statement specifically includes: converting the first search result from a byte stream format into a tree structure; and determining each document identifier based on the converted first search result.

[0069] In practical applications, byte streams are composed of binary codes. After converting the byte stream into a string format according to a predetermined encoding rule, the string is then converted into a corresponding tree structure, which can facilitate parsing the document identifier associated with the search statement from the tree structure, thereby achieving efficient and accurate extraction of document identification information. This method improves the accuracy and stability of data parsing, reduces the complexity and error risks that may be caused by direct processing of byte streams, and provides a clear structured data foundation for subsequent data processing and analysis, improving the overall performance and user experience of the retrieval system.

[0070] In one embodiment, converting the first search result from a byte stream format into a tree structure specifically includes: based on a predetermined encoding format, decoding the first search result in a byte stream format to generate a first search result in a string format; identifying tags in the first search result in a string format, and based on the tags, constructing a nested relationship of the first search result; based on the nested relationship, converting the first search result into a first search result in a tree structure.

[0071] Specifically, in this embodiment, the first search result is encoded based on the HTML format. In the HTML format, the nesting of character strings is marked with tags, including a start tag and an end tag, wherein the start tag is used to indicate the start of a node, and the end tag is used to indicate the end of a node. After each node is identified, the nesting relationship of each node time can be determined based on the corresponding position of each node. For example The tag is the start tag. is the end tag, and the content between the two tags is the child node corresponding to the div node, that is, the node nested in the div node. By decoding the search results in byte stream format to generate a string, and building a nested relationship based on tag recognition, the search results are converted into a tree structure, which significantly improves the efficiency of data organization and parsing. The construction of the tree structure makes the data hierarchy clearer and facilitates subsequent access, screening and operation, thereby improving the processing accuracy and stability of the search results, and at the same time laying a structured foundation for the analysis and utilization of complex data.

[0072] In one embodiment, the identification of tags in the first search result in string format and the construction of a nested relationship of the first search result based on the tags specifically include: identifying all start tags and all end tags in the first search result; matching each start tag and each end tag one by one with each other; determining whether the start tag A is after the start tag B and whether the end tag A is before the end tag B, and if so, determining that tag A is nested inside tag B.

[0073] Specifically, in this embodiment, the first search result exists in the form of a node, and the content between the two corresponding tags belongs to the child node corresponding to this node. Therefore, in the actual identification process, the parent node is first identified. When the parent node meets the screening conditions, the child nodes within it also meet the screening conditions. By matching the start tag and the end tag and judging the position relationship, the nested hierarchical relationship between the nodes can be quickly and accurately parsed. This rule-based parsing method reduces complex operations and improves parsing efficiency; by identifying the parent node and screening the qualified parent node, the data of its child nodes can be automatically included. It avoids repeated screening of child nodes, simplifies the logic, and improves screening efficiency.

[0074] In one embodiment, determining each document identifier based on the converted first search result specifically includes: identifying all ID sub-nodes nested in the IDLIST node in the first search result of the tree structure; and obtaining the document identifier contained in each ID sub-node.

[0075] Specifically, after the first search result is converted into a tree structure, all child nodes under the IDLIST node correspond to an ID number, and based on this ID number, further literature data can be obtained in the database.

[0076] In one embodiment, the step of writing corresponding second resource locators based on the document identifiers, sending the second resource locators to a predetermined life science document database for retrieval, and receiving second search results returned by the life science document database specifically includes:

[0077] Each document identifier is filled into the predetermined URL-encoded search formula, and then resent to the PubMed database for retrieval to obtain the document metadata corresponding to each document identifier.

[0078] Specifically, Figure 5 Fill in the 6 document identifiers in the predetermined URL-encoded search formula, and then send a search request to the database to receive the document metadata returned by the database. Repeat the request 6 times to receive the document metadata corresponding to the 6 document data. Take the first document ID "30830411" as an example and continue to build the search URL.

[0079] You can get an XML file that records the metadata of the document, including title, abstract, DOI number, PubMedID and other information. Figure 6 The content described in is the document metadata returned for the document ID "30830411".

[0080] In one embodiment, parsing the second search result to obtain document data corresponding to the document identifier specifically includes: converting the second search result from a byte stream format into a tree structure; identifying an articletitle node, an abstracttext node, and an articleid[@idtype='doi'] node in the second search result of the tree structure; extracting the corresponding document title based on the articletitle node; extracting the corresponding document abstract based on the abstracttext node; and extracting the corresponding unique identifier based on the articleid[@idtype='doi'] node.

[0081] Specifically, if Figure 6 As shown, the search results returned by the second search result are the same as the first search result, both of which are byte streams. In order to identify the content in the second search result, the second search result also needs to be decoded to generate the corresponding search results in string format, and then based on the nested relationship of the search results in string format, the articletitle node, abstracttext node and articleid[@idtype='doi'] node are obtained accordingly, which represent the document title node, document abstract node and document DOI number node respectively. Among them, the DOI number represents the digital object unique identifier, which is a globally unique code. Therefore, obtaining the DOI number can ensure that the document will not be repeated.

[0082] In one embodiment, it also includes: after obtaining the second search result and generating structured breath metabolite data, the second resource locator is used as a key and the corresponding structured breath metabolite data is used as a value, and stored in the cache mechanism in the form of a key-value pair. When the data generation method is interrupted and needs to be continued, the following operations are performed to determine whether there is an identical second resource locator in the cache mechanism: the keys in the cache mechanism (i.e., the stored second resource locators) are traversed, and the second resource locator to be determined is compared with the keys in the cache one by one by string matching. If a key that is exactly the same as the second resource locator is found during the traversal process, it is determined that the same second resource locator is stored in the cache mechanism. If the same key is not found after traversing the entire cache mechanism, it is determined that the same second resource locator does not exist in the cache mechanism, and then the second resource locator is sent to a predetermined life science literature database for retrieval.

[0083] Specifically, in order to improve the efficiency of obtaining document data, in the process of obtaining document data, in addition to using the program to automatically execute the acquisition process, it is also necessary to continue to execute based on the original data when the program crashes or an error occurs, rather than starting from the beginning, to avoid data duplication and reduce the time wasted by repeated data acquisition. To achieve this purpose, it is necessary to set up a cache mechanism in addition to setting up a database, which is used to store data corresponding to search statements that have not yet been executed, wherein the second resource locator is set as key data, the document data obtained by the second resource locator is set as value data, the key data and the value data are stored in the cache mechanism, and before using the program to send a search request to the database, it is searched in the cache mechanism whether there is the same second resource locator. If it exists, it means that the second resource locator has been searched, and there is no need to send a request to the database through the Internet to search again. It is only necessary to return the search results stored in the cache mechanism to structure it.

[0084] In one embodiment, the search statement includes: a first keyword for limiting the research object, a second keyword for limiting the detection method, a third keyword for limiting the detection sample type, and a fourth keyword for limiting the target disease or physiological state.

[0085] In practical applications, in order to clarify the research object and data scope, the applicant shall define it from the following key dimensions: 1. Research object: The first keyword is used to limit the research object to humans, excluding breath metabolite data related to other organisms (such as animals, plants or microorganisms). This step ensures that the research data is highly targeted and meets the characteristics and research objectives of human breath metabolites. 2. Detection method: The second keyword is used to limit the detection method to mass spectrometry. Mass spectrometry technology is widely used in the qualitative and quantitative analysis of metabolites due to its high sensitivity and high resolution. Through mass spectrometry technology, the type and concentration information of breath metabolites can be accurately captured, thus providing a reliable basis for disease diagnosis or physiological status assessment. 3. Detection sample type: The third keyword is used to limit the type of detection sample to exhaled gas or exhaled condensate, and clearly exclude other types of samples (such as blood, saliva, urine or cerebrospinal fluid, etc.). Exhaled gas and exhaled condensate are the main biological samples used in breath metabolomics research. This limitation helps to focus on the unique characteristics of exhaled metabolites and exclude interference from other body fluid samples. 4. Target disease or physiological state: The fourth keyword is used to limit the target disease or physiological state of the study. The detection target of exhaled breath metabolites is usually to judge the physiological state of an organism or whether it suffers from a specific disease based on its type and concentration changes. By clarifying the target disease, we can further screen exhaled breath metabolites related to the disease, analyze the changing patterns of their types and concentrations, and explore potential biomarkers. This process not only provides a scientific basis for the early diagnosis of the disease, but also may reveal new pathological mechanisms.

[0086] Taking the search for breath research related to prostate cancer as an example, this study used the following search statement: "(((breath[Title / Abstract] OR exhaled air[Title / Abstract]OR exhaled breathcondensate[Title / Abstract]) AND (prostate cancer[Title / Abstract])) AND (massspectrometry[Title / Abstract])) AND (humans[Filter])".

[0087] The part (breath[Title / Abstract] OR exhaled air[Title / Abstract] OR exhaledbreath condensate[Title / Abstract]) indicates that the keywords exhaled air or exhaled breath condensate must appear in the title or abstract of the limited document.

[0088] The (prostate cancer [Title / Abstract]) part indicates that prostate cancer must appear in the title or abstract of the document. When searching for other diseases, it will be replaced by other disease names. If there are synonyms, multiple synonyms need to be connected with OR.

[0089] The (mass spectrometry [Title / Abstract]) part indicates that mass spectrometry technology must appear in the title or abstract of the restricted literature.

[0090] The (humans[Filter]) part limits the literature to those whose research subjects are humans.

[0091] In one embodiment, only the fourth keyword is different between the search statements.

[0092] In practical applications, although Python programs can be used to efficiently obtain a large amount of literature metadata in a short time, in order to rationally plan the workflow of manual reading and analysis of literature, it is necessary to ensure that the number of literature obtained in each batch remains within a controllable range. At the same time, the logic of literature retrieval not only needs to conform to the specific content characteristics of breath metabolomics literature, but also needs to have high repeatability to ensure the reliability and scientificity of subsequent research. Based on this, this application proposes a batch literature metadata acquisition strategy with human diseases and physiological states as the core. Specifically, in the design of the search statement, the disease name is used as the main variable, and the breath metabolomics research literature related to different diseases is gradually obtained by replacing the disease keywords in the search statement. On the one hand, this method can gradually cover multiple research directions in the field of breath metabolomics, and on the other hand, it can ensure that the content included in the database is standardized and logically consistent. In addition, in order to improve the efficiency of retrieval work and the quality of literature data, this application also adjusts the keywords and result quantity parameters of each search to ensure that the number of literature obtained in each batch is within a range suitable for manual reading and analysis to avoid information overload.

[0093] Secondly, in order to ensure the repeatability of the data acquisition process, by recording and reusing fixed retrieval logic templates in different batches of retrieval, it is ensured that subsequent repeated operations can generate consistent results, providing technical guarantee for the reproducibility of literature analysis.

[0094] In one embodiment, the screening of the literature data based on a predetermined screening condition specifically includes:

[0095] Determine whether the document data includes the following four types of information:

[0096] The name of the human disease being studied, the differential breath metabolites associated with the disease, the variation characteristics of the differential breath metabolites in the experimental groups, and the type of breath sample;

[0097] If the document data lacks any of the above information, it is determined that the document data does not meet the screening conditions.

[0098] In practical applications, even if the content of the literature is initially limited by the search statement, it is impossible to completely avoid retrieving literature that is not related to the research purpose. Therefore, it is necessary to screen the retrieved literature in stages. First, by reading the abstract of the literature, determine whether its content involves human exhaled metabolites. If the abstract of the literature does not mention information related to human exhaled metabolites, the possibility of the literature as a candidate target literature can be directly excluded. After completing the abstract screening, the main content of the literature that has passed the preliminary screening is further analyzed, focusing on determining whether the literature contains the following four types of key information: the name of the human disease studied, the differential exhaled metabolites related to the disease, the change characteristics of the differential exhaled metabolites in the experimental group, and the type of exhaled sample. Through this screening step, the literature that meets the research requirements can be accurately identified to ensure that it is suitable as a data source for the exhaled metabolite database, laying a reliable data foundation for subsequent research.

[0099] In one embodiment, the generating structured breath metabolite data by using the literature data meeting the screening conditions specifically includes:

[0100] Based on the screened literature data, the name of the human disease being studied, the differential exhaled metabolites associated with the disease, the change characteristics of the differential exhaled metabolites in the experimental group, the type of exhaled sample, and the title corresponding to the literature data were obtained to construct an exhaled metabolite data table.

[0101] In practical applications, standardized management of breath metabolite data can be achieved by extracting key information from the screened literature data and generating structured data tables. By extracting and integrating core information from the literature (such as disease names, differential breath metabolites, change characteristics, and sample types), this solution effectively improves the availability of data. The screened data is only related to the target disease and metabolites, avoiding interference from irrelevant information. Structured data tables have good sharing and reusability: users in different research fields can also use these data to carry out various studies such as disease diagnosis and biomarker development.

[0102] In one embodiment, constructing a breath metabolite data table specifically includes:

[0103] Convert the human disease name into the corresponding disease preferred name and corresponding MeSHID in the MeSH database;

[0104] Convert the differential breath metabolites into the corresponding compound preferred names and corresponding InChIKeys in PubChem;

[0105] The change characteristics specifically include: increase, decrease and no significant change;

[0106] The types of breath samples specifically include: exhaled gas or exhaled condensate;

[0107] The titles were converted into document titles and corresponding PubMed IDs.

[0108] In practical applications, such as Figure 7 As shown in the figure, the data table specification of the exhaled metabolites generated in this embodiment is as follows. Specifically, the names of human diseases are unified into standardized entry names and MeSH IDs in the MeSH database, which can avoid information confusion caused by synonyms or different naming methods; differential exhaled metabolites are associated with PubChem compound names and InChIKeys, providing a unified chemical identifier.

[0109] Providing a corresponding PubMed ID for each document allows researchers to quickly locate the original document and achieve efficient retrieval. The introduction of PubMed ID also enhances the academic credibility and verifiability of data tables.

[0110] By classifying the characteristics of changes in exhaled metabolites (increase, decrease, no significant change), a concise description of the dynamic changes of metabolites is provided. This classification can quickly convey research results and provide support for subsequent pattern recognition and disease association analysis. Clearly distinguishing the types of exhaled samples ensures the applicability of the data under different experimental conditions. The annotation of sample types facilitates researchers to screen data according to experimental needs and improve the practical value of the data. By introducing information from authoritative databases such as MeSH, PubChem and PubMed, data from different sources are integrated into one table to provide users with rich multidimensional data. This data processing method avoids the tedious process of researchers manually searching between multiple databases and improves efficiency.

[0111] In one embodiment, the method of determining whether the document data includes the following four types of information includes: inputting the acquired document data into a pre-trained artificial intelligence model, performing semantic recognition on the full-text information to determine whether the document data includes the target information.

[0112] Specifically, in order to improve the efficiency of reading and judging documents, a pre-trained artificial intelligence model can be used to identify whether the document data contains target information. The artificial intelligence model used can be BERT, Longformer, GPT, BiLSTM and other models in the prior art. The specific model can be selected according to the characteristics of the model and the actual situation of the training computing resources, which will not be elaborated in this application.

[0113] Embodiment 2

[0114] See also Figure 2 , a breath metabolite data acquisition device, the breath metabolite data acquisition device comprising:

[0115] Construction unit 1, for constructing a search statement associated with breath metabolites;

[0116] A first search unit 2 is used to convert a first resource locator for document retrieval based on the search statement, send the first resource locator to a predetermined life science document database for retrieval, receive a first search result returned by the life science document database; parse the first search result to obtain all document identifiers associated with the search statement;

[0117] A second search unit 3 is used to convert each of the document identifiers into a corresponding second resource locator, send the second resource locator to a predetermined life science document database for search, receive a second search result returned by the life science document database; parse the second search result to obtain document data corresponding to the document identifiers one by one;

[0118] The generating unit 4 is used to screen the literature data based on a predetermined screening condition; and generate structured breath metabolite data using the literature data that meets the screening condition.

[0119] For the specific definition of the breath metabolite data acquisition device, please refer to the definition of the breath metabolite data acquisition method above, which will not be repeated here. Each module in the above breath metabolite data acquisition device can be implemented in whole or in part by software, hardware and a combination thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0120] Those skilled in the art will understand that Figure 2The structure shown in the figure is only a block diagram of a part of the structure related to the present application scheme, and does not constitute a limitation of the present application scheme. The specific breath metabolite data acquisition device may include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0121] Embodiment 3

[0122] A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for acquiring breath metabolite data as described in Example 1 is implemented.

[0123] Embodiment 4

[0124] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. When the computer program is executed by the processor, a method for acquiring exhaled metabolite data is implemented.

[0125] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0126] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0127] S1. Construct search statements related to breath metabolites;

[0128] S2, converting a first resource locator for document retrieval based on the search statement, sending the first resource locator to a predetermined life science document database for retrieval, and receiving a first search result returned by the life science document database; parsing the first search result to obtain all document identifiers associated with the search statement;

[0129] S3, based on each of the document identifiers, converting them into corresponding second resource locators, sending the second resource locators to a predetermined life science document database for retrieval, receiving second retrieval results returned by the life science document database; parsing the second retrieval results to obtain document data corresponding to the document identifiers one by one;

[0130] S4. Filter the literature data based on predetermined filtering conditions; and generate structured breath metabolite data using the literature data that meets the filtering conditions.

[0131] In one embodiment, parsing the first search result to obtain all document identifiers associated with the search statement specifically includes: converting the first search result from a byte stream format into a tree structure; and determining each document identifier based on the converted first search result.

[0132] In one embodiment, converting the first search result from a byte stream format into a tree structure specifically includes: based on a predetermined encoding format, decoding the first search result in a byte stream format to generate a first search result in a string format; identifying tags in the first search result in a string format, and based on the tags, constructing a nested relationship of the first search result; based on the nested relationship, converting the first search result into a first search result in a tree structure.

[0133] In one embodiment, the identification of tags in the first search result in string format and the construction of a nested relationship of the first search result based on the tags specifically include: identifying all start tags and all end tags in the first search result; matching each start tag and each end tag one by one with each other; determining whether the start tag A is after the start tag B and whether the end tag A is before the end tag B, and if so, determining that tag A is nested inside tag B.

[0134] In one embodiment, determining each document identifier based on the converted first search result specifically includes: identifying all ID sub-nodes nested in the IDLIST node in the first search result of the tree structure; and obtaining the document identifier contained in each ID sub-node.

[0135] In one embodiment, parsing the second search result to obtain document data corresponding to the document identifier specifically includes: converting the second search result from a byte stream format into a tree structure; identifying an articletitle node, an abstracttext node, and an articleid[@idtype='doi'] node in the second search result of the tree structure; extracting the corresponding document title based on the articletitle node; extracting the corresponding document abstract based on the abstracttext node; and extracting the corresponding unique identifier based on the articleid[@idtype='doi'] node.

[0136] In one embodiment, it also includes: after obtaining the second search result and generating structured breath metabolite data, the second resource locator is used as a key and the corresponding structured breath metabolite data is used as a value, and stored in the cache mechanism in the form of a key-value pair. When the data generation method is interrupted and needs to be continued, the following operations are performed to determine whether there is an identical second resource locator in the cache mechanism: the keys in the cache mechanism (i.e., the stored second resource locators) are traversed, and the second resource locator to be determined is compared with the keys in the cache one by one by string matching. If a key that is exactly the same as the second resource locator is found during the traversal process, it is determined that the same second resource locator is stored in the cache mechanism. If the same key is not found after traversing the entire cache mechanism, it is determined that the same second resource locator does not exist in the cache mechanism, and then the second resource locator is sent to a predetermined life science literature database for retrieval.

[0137] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0138] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0139] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A method for acquiring breath metabolite data, characterized in that: include: S1. Construct search statements related to breath metabolites; S2, converting a first resource locator for literature search based on the search statement, sending the first resource locator to a predetermined life science literature database for search, and receiving a first search result returned by the life science literature database; parsing the first search result to obtain all literature identifiers associated with the search statement; the life science literature database is specifically a PubMed database; The parsing of the first search result to obtain all document identifiers associated with the search statement specifically includes: Converting the first search result from a byte stream format into a tree structure; converting the first search result from a byte stream format into a tree structure specifically includes: Based on a predetermined encoding format, decoding the first search result in byte stream format to generate a first search result in character string format; Identify a tag in a first search result in a string format, and construct a nested relationship of the first search result based on the tag; Based on the nested relationship, converting the first search result into a first search result with a tree structure; Determine each document identifier based on the converted first search result; the step of determining each document identifier based on the converted first search result specifically includes: Identify all ID child nodes nested in the IDLIST node in the first search result of the tree structure; Get the document identifier contained in each ID child node; S3, based on each of the document identifiers, converting them into corresponding second resource locators, sending the second resource locators to a predetermined life science document database for retrieval, and receiving second retrieval results returned by the life science document database; parsing the second retrieval results to obtain document data corresponding to the document identifiers one by one; the parsing of the second retrieval results to obtain document data corresponding to the document identifiers one by one specifically includes: Converting the second search result from a byte stream format into a tree structure; Identify the articletitle node, the abstracttext node, and the articleid[@idtype='doi'] node in the second search result of the tree structure; Extract the corresponding document title based on the articletitle node; Extracting the corresponding document abstract based on the abstracttext node; Extract the corresponding unique identifier based on the articleid[@idtype='doi'] node; S4. Screening the literature data based on a predetermined screening condition; generating structured breath metabolite data using the literature data that meets the screening condition; After obtaining the second search result and generating structured breath metabolite data, the second resource locator is used as a key and the corresponding structured breath metabolite data is used as a value, and stored in the cache mechanism in the form of a key-value pair; when the data generation method is interrupted and needs to be continued, the following operations are performed to determine whether the same second resource locator exists in the cache mechanism: the keys in the cache mechanism are traversed, and the second resource locator to be determined is compared with the keys in the cache one by one for string matching; if a completely identical key is found during the traversal process, it is determined that the same second resource locator is stored in the cache mechanism; if the same key is not found after traversing the entire cache mechanism, it is determined that the same second resource locator does not exist in the cache mechanism, and then the second resource locator is sent to a predetermined life science literature database for retrieval.

2. A method for acquiring breath metabolite data according to claim 1, characterized in that: The identifying a tag in the first search result in a string format and constructing a nested relationship of the first search result based on the tag specifically includes: Identify all start tags and all end tags in the first search result; match each start tag and each end tag one by one; Determine whether the start tag A is located after the start tag B, and whether the end tag A is located before the end tag B. If so, determine that tag A is nested inside tag B.

3. A breath metabolite data acquisition device, characterized in that: The device comprises: A building block for constructing search statements related to breath metabolites; The first search unit is used to convert a first resource locator for document retrieval based on the search statement, send the first resource locator to a predetermined life science document database for retrieval, and receive a first search result returned by the life science document database; parse the first search result to obtain all document identifiers associated with the search statement; the life science document database is specifically a PubMed database; the parsing of the first search result to obtain all document identifiers associated with the search statement specifically includes: Converting the first search result from a byte stream format into a tree structure; converting the first search result from a byte stream format into a tree structure specifically includes: Based on a predetermined encoding format, decoding the first search result in byte stream format to generate a first search result in character string format; Identify a tag in a first search result in a string format, and construct a nested relationship of the first search result based on the tag; Based on the nested relationship, converting the first search result into a first search result with a tree structure; Determine each document identifier based on the converted first search result; the step of determining each document identifier based on the converted first search result specifically includes: Identify all ID child nodes nested in the IDLIST node in the first search result of the tree structure; Get the document identifier contained in each ID child node; A second search unit is used to convert each of the document identifiers into a corresponding second resource locator, send the second resource locator to a predetermined life science document database for search, receive a second search result returned by the life science document database; parse the second search result to obtain document data corresponding to the document identifiers one by one; A generating unit is used to screen the document data based on a predetermined screening condition; generate structured breath metabolite data using the document data that meets the screening condition; after obtaining the second search result and generating the structured breath metabolite data, use the second resource locator as a key and the corresponding structured breath metabolite data as a value to store in a cache mechanism in the form of a key-value pair; when the data generating method is interrupted and needs to be continued, perform the following operations to determine whether there is an identical second resource locator in the cache mechanism: traverse the keys in the cache mechanism, and perform string matching comparisons on the second resource locator to be determined and the keys in the cache one by one; if a completely identical key is found during the traversal process, it is determined that the identical second resource locator is stored in the cache mechanism; if the identical key is not found after traversing the entire cache mechanism, it is determined that the identical second resource locator does not exist in the cache mechanism, and then the second resource locator is sent to a predetermined life science literature database for retrieval.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for acquiring breath metabolite data according to claim 1 is implemented.

5. A computer, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the method for acquiring breath metabolite data according to claim 1 is implemented.

Images