16 results about "Biological entity" patented technology

The invention discloses a biomolecular interaction prediction method based on multi-modal attention fusion, and belongs to the technical field of artificial intelligence drug discovery. The method comprises the following steps: acquiring multi-modal characteristics of drugs, targets, diseases and genes: sequence structure characteristics, 3D structure characteristics, similarity network characteristics and biological relation network embedding characteristics; constructing a feature fusion prediction model, and performing training; inputting the multi-modal features of the two biological entities into the trained feature fusion prediction model, and outputting the probability of interaction of the two biological entities; the feature fusion prediction model comprises a Transform encoder and an MLP (Markup Language Protocol) network; the multi-modal features are input into a feature fusion prediction model for stacking and then are input into a Transform encoder, the features are processed by using a multi-head self-attention mechanism, and an output result is flattened and subjected to dimension reduction processing to obtain embedded vector representation; and finally, performing element corresponding multiplication on the embedded vectors of the two biological entities, inputting the embedded vectors into an MLP network, and outputting an interaction probability between the two biological entities.

The invention discloses a biological entity multivariate association prediction system combining linear and nonlinear fusion matrix factorization, which relates to the technical field of biological entity multivariate association prediction and comprises a multivariate biological entity input module, a cross-modal feature extraction module, a dual-channel fusion matrix factorization module, a dynamic feature fusion device and a multivariate association prediction engine. According to the method, the limitation of a traditional biological entity association prediction method is broken through by fusing linear and nonlinear matrix decomposition technologies, and a dynamic feature fusion mechanism realizes optimal combination of cross-modal features through adaptive weight adjustment, so that the problem of insufficient flexibility of a traditional static fusion strategy is overcome; in addition, the system adopts a three-dimensional tensor modeling technology, a unified prediction framework of multiple types of associations such as gene-disease, drug-target, metabolite-pathway and the like is realized, heterogeneity characteristics of biological associations can be effectively maintained, and compared with the prior art, the analysis capability of a complex biological network is remarkably improved.

A computer-implemented method is provided of analysing an image of a tissue specimen, which method comprises steps including detecting in the image each of a primary type of biological entity, detecting in the image one or more secondary types of biological entity, associated with each of the detected primary biological entities, generating an entity graph, in which each graph node is assigned to a primary biological entity, and in which each graph node is associated with predictive features measured from the image in respect of that graph node, including at least one predictive feature that is measured relative to two or more types of biological entity.

A device is provided for facilitating the imaging of biological entities. The device includes a body having upper and lower surfaces. The body defines a channel for accommodating fluid flow therethrough and a well communicating with the channel and the lower surface. A microscopy grid is removably received in the body and has a first side communicating with the channel and a second side directed at well. A retainer extends into the channel and is engageable with the microscopy grid to support the microscopy grid.

A monitoring device (1) comprises a microwell plate (10) comprising microwells (20) dimensioned to hold a respective biological entity and a channel plate (30) comprising at least one fluid channel (40, 42) in fluid connection with a fluid input port (11; 11A, 11B) and a fluid output port (13; 13A, 13B). An optode composition (25) is present in the microwells (20) and comprising an analyte-sensitive optode compound generating a detectable optical signal in response to an analyte. The channel plate (30) and the microwell plate (10) are slidably arranged together and movable relative to each other between a loading position, in which the at least one fluid channel (40, 42) is in fluid connection with at least a subset (21, 23) of the plurality of microwells (25), and a monitoring position, in which the channel plate (30) closes the at least a subset (21, 23) of the plurality of microwells (20).

Disclosed is a biological entity for treating brain tumors, particularly gliomas, and a vector comprising the biological entity. The biological entity is a construct comprising at least an anti-tumor transgene, which is composed of at least one GluA knockdown agent, preferably at least two GluA knockdown agents, and preferably further comprises a fusion protein, an anti-PD-1 antibody, an anti-CTLA-4 antibody, and an immune response promoter of IL12. The vector comprises a wild-type HSV-1 virus, and the biological entity replaces the ICP34.5 gene of the wild-type HSV-1 virus. The vector comprising the wild-type HSV-1 virus modified with the biological entity shows little negative effect on neurons, while showing positive effect on human glioblastoma cells, both when neuronal cells and glioblastoma cells are cultured separately and when co-cultured.

The invention relates to the field of additive manufacturing, in particular to electrode ink based on a biological entity and a preparation method and application of the electrode ink. The preparation method of the electrode ink based on the biological entity comprises the following steps: performing degreasing pretreatment on the biological entity to obtain a degreased biological entity; uniformly mixing the degreased biological entity with a binder and a structure reinforcing phase to obtain a mixture; and mixing and grinding the mixture and water to obtain the electrode ink based on the biological entity, the biological entity is plant pollen. The electrode ink provided by the invention has a biological entity with relatively high loading capacity, and can provide sufficient ion transmission gap channels; the electrode ink disclosed by the invention has good printable performance, and can keep a stable structure after pyrolysis and carbonization without cracking, and an electrode precursor frame prepared by printing is controllable in form, good in formability and high in stability.

This invention belongs to the field of biological data processing technology and discloses a multi-omics data fusion method and system based on hypergraph networks. This invention uses a hypergraph structure to model omics data, enabling more efficient mining of high-order interaction information and a more comprehensive revelation of complex relationship patterns between biological entities. By introducing a hypergraph aggregation mechanism, it can more accurately represent the high-order complex relationships within various omics data, overcoming the limitations of traditional methods that can only capture pairwise node relationships, and improving the model's ability to understand the interactions between biological entities. This hypergraph fusion method effectively integrates multi-omics-specific hypergraphs, achieving a unified representation of cross-omics data, enhancing the model's ability to represent complex multi-relationship data, and providing more comprehensive technical support for various downstream tasks.

The present invention provides a system for receiving biological sequence information and activating the synthesis of a biological entity. The system has a receiving unit for receiving a signal encoding biological sequence information transmitted from a transmitting unit. The transmitting unit can be present at a remote location from the receiving unit. The system also has an assembly unit connected to the receiving unit, and the assembly unit assembles the biological entity according to the biological sequence information. Thus, according to the present invention biological sequence information can be digitally transmitted to a remote location and the information converted into a biological entity, for example a protein useful as a vaccine, immediately upon being received by the receiving unit and without further human intervention after preparing the system for receipt of the information. The invention is useful, for example, for rapidly responding to viral and other biological threats that are specific to a particular locale.

A computer-implemented method of electronically mining medical and scientific datasets to determine a ranking indicating a level of evidence for an association between two entities is disclosed. The method comprises receiving a representation of an entity pair, performing first data mining on one or more unstructured datasets to generate one or more first scores each representing an extent of association between the entities of the entity pair, and performing second data mining on one or more structured datasets to generate one or more second scores each representing an extent of association between the entities of the entity pair. The method also comprises using a classifier to determine a predicted ranking for the entity pair using the one or more first scores and the one or more second scores, and providing the predicted ranking to a user as an indication of the strength of evidence for an association between the entities of the entity pair.

A computer-implemented method of training a machine learning model to identify biological entities for drug discovery is disclosed. The method comprises providing a training data set comprising a plurality of entity-linked text sequences, each text sequence including a mention of a biological entity, where the biological entity is linked to a corresponding biological entity identifier from a set of possible biological entity identifiers; masking the mention of the biological entity within each text sequence; encoding each masked text sequence into an input representation for a machine learning model; and training a machine learning model to predict the unique entity identifier of the masked biological entity based on the input representation. The described method is able to utilise the full breadth of the rich contextual information available in the biomedical text corpus to predict new biological targets for drug discovery and avoids the restrictions intrinsic to relationship prediction using knowledge graphs. The ability to identify more promising, biologically relevant targets in an automated manner, significantly reduces the requirement of human input and reduces the failure rate in targets that are progressed in the drug delivery pipeline.

This disclosure provides a cell annotation method, apparatus, electronic device, and storage medium. The method includes: constructing biological knowledge relation data, wherein the biological knowledge relation data represents the biological relationships between different biological entities; acquiring gene expression data of a target sample, determining a target gene based on the gene expression data, and identifying the biological entity corresponding to the target gene in the biological knowledge relation data as a starting node; performing relation reasoning in the biological knowledge relation data based on the starting node to determine a target node, and performing cell annotation based on the biological entity represented by the target node. The embodiments of this application can improve the flexibility of cell annotation.

The present invention describes an integrated acoustic spectroscopy system and method for the analysis, characterization, and classification of organic, inorganic, and biological matter, assisted by supervised artificial intelligence, through the analysis of samples obtained from specific entities. The system integrates a computing unit (2) with a graphical interface (1), a function generator (3), a signal processing device (4), and an acoustic coupling clamp (5) with coaxial transducers. As an application example, glass samples (from inorganic entities), culture media (from organic entities), and cell culture lines (from biological entities) were successfully characterized and classified.The method involves capturing a digitized acoustic signature from a sample, generating a multidimensional data hierarchy that ranges from time-domain signals to high-density spectrograms obtained by Continuous Wavelet Transform (CWT) and post-processing vectors for classification. Using a 2% rescaling, standardization, and dimensionality reduction (UMAP) chain, an acoustic signature is extracted and fed into a Support Vector Machine (SVM) model optimized by Bayesian inference. The invention is notable for a management and synchronization module that allows for incremental retraining of the model through manual labeling in the dynamic repository. The system enables the differentiation of healthy and pathological cell phenotypes with a Matthews Correlation Coefficient (MCC) greater than 0.94 in less than a minute, optimizing the analysis, characterization and classification of matter without dependence on reagents or complex infrastructures, with outstanding application in cancer screening.

The invention discloses a stacked comparative generative adversarial network-based biological entity multivariate association accurate prediction system, which comprises a biological data module, a learning module, a multi-layer network module, a multivariate relationship modeling module, a biological association module and a data verification module, wherein the generation module comprises genome data, proteome data, literature data and drug information and performs data cleaning and formatting, and the learning module is used for generating a learning model, learning comparison of relationships between different biological entities in a high-dimensional space through generalization ability of the model, and adjusting a generator according to a comparison result; through the arrangement of the biological data module, the learning module, the multi-layer network module, the multivariate relation modeling module, the biological association module and the data verification module, the system can effectively improve the generalization ability and robustness of the model by utilizing the mutual game of the generator and the discriminator in the generative adversarial network.

This invention relates to a smart curtain monitoring method, system, and computer-readable storage medium. The smart curtain monitoring method of this invention includes the following steps: acquiring image information of a monitoring area surrounding the curtain, as well as biological information within the monitoring area and the distance information of the biological information from the curtain; determining whether a biological entity is approaching the curtain based on the biological information, determining whether the biological entity is a preset protected target based on the image information, and determining whether the distance between the biological entity and the curtain is below a preset distance threshold based on the distance information; if a biological entity is approaching the curtain, and the biological entity is the preset protected target, and the distance is below the preset distance threshold, then issuing a control signal to close the curtain. The smart curtain monitoring method of this invention has the advantages of real-time monitoring of whether a protected target is approaching the curtain and reducing the risk of accidental falls near the curtain.

The present subject matter relates to a visualization system, that employs advanced imaging technologies and see-through smart devices, in order to assist the user, to visualise, localise and detect foreign entities within a biological entity, by directly providing real-time processed information to a user. This system is designed / created to enhance the accuracy and efficiency of medical / research procedures / investigations, by clearly differentiating in between, a biological entity (including, all components, parts, portions of the biological entity), and, any kind of foreign entities disposed within the said biological entity. Further, the innovative system enables simple non-invasive method of detecting and visualising, a foreign entity that is disposed within a biological entity.