29 results about "Biological network" patented technology

The invention discloses a drug relocation method and system based on heterogeneous knowledge and structure fusion, and belongs to the technical field of medical care informatics. The method comprises the following steps: constructing a biomedical domain knowledge heterogeneous graph; generating disease knowledge embedding and drug knowledge embedding corresponding to a target drug-disease pair based on the biomedical domain knowledge heterogeneous graph; generating disease structure embedding and drug structure embedding corresponding to the target drug-disease pair by constructing a drug-drug similarity network, a disease-disease similarity network and a drug-disease association network; and based on disease knowledge embedding, drug knowledge embedding, disease structure embedding and drug structure embedding, obtaining a drug relocation result. According to the method, complex biological network characteristics are accurately captured and complex entity information is finely modeled through an innovative drug relocation model, so that accurate drug relocation is realized.

The invention belongs to the technical field of biological information, and particularly relates to a cancer molecular subtype recognition method based on self-adaptive pellet multi-view image clustering. The method comprises the following steps: acquiring a multi-omics data set of cancer molecules, and constructing a pellet set for each kind of omics data in the multi-omics data set; constructing a biological network structure chart according to the particle ball set; fusing the biological network structure diagrams of the omics data to obtain a unified graph; inputting the unified graph into a pre-trained heterogeneous graph neural network for processing to obtain a cancer molecular subtype recognition result; according to the method, multi-scale biological structure features in multiple omics data can be captured at the same time, collaborative optimization of molecular network topology and patient characterization is achieved, and therefore the accuracy of cancer molecular subtype recognition results is improved.

The invention discloses a multi-layer heterogeneous network unicellular organism network inference method based on meta-path enhancement, which mainly comprises a gene regulation knowledge base enhanced multi-layer heterogeneous network construction module for integrating an external gene interaction network and multiple omics data such as scRNA-seq, scATAC-seq, ST and the like; constructing a single-cell multi-omics multilayer heterogeneous network containing cell-cell, cell-gene and gene-gene relationships, and fusing spatial constraints to consider cell positions and tissue structures; and the feature enhancement module based on the meta-path explores complex semantics of the network by designing a multi-hop meta-path mode, designs an adaptive multi-view learning framework and a multi-round enhancement mechanism, and optimizes feature representation by using cell-gene interaction and cross-modal attention fusion. The unicellular biological network can be effectively deduced, the deduction accuracy and biological interpretation are remarkably improved, the method plays an important role in understanding the cell biological process, developing and treating diseases and the like, has good expandability, and can further integrate multi-modal omics data such as proteomics and metabonomics.

The invention belongs to the technical field of drug-target relationship prediction, and relates to a prediction method and device for interaction between a drug and a target, and a storage medium. The method comprises the following steps: constructing a heterogeneous biological network diagram based on drugs, targets, diseases, side effects and interaction of the drugs, the targets, the diseases and the side effects, and obtaining an initial feature vector of each node; taking the drug nodes and the target nodes as target nodes, and obtaining four types of neighbor nodes and four types of meta path nodes of the target nodes; performing feature extraction on the initial feature vectors of the four types of neighbor nodes of the target node and the four types of meta-path nodes to obtain a neighbor view embedding representation and a meta-path view embedding representation; projecting the neighbor view embedded representation and the meta-path view embedded representation into a query vector, a key vector and a value vector, and calculating a cross attention value; based on the neighbor view embedded representation, the meta-path view embedded representation and the cross attention value of each target node, obtaining a fusion representation; and performing interaction prediction of the drug node and the target node based on the fused representation.

The invention relates to a structural network-genetic map biological network model for predicting ischemic stroke and a construction method thereof, and the method comprises the steps: extracting and calculating seven multi-scale morphological features and pairwise Pearson correlation coefficients among the features from T1 weighted imaging data and diffusion tensor imaging data; constructing a 308 * 308 morphological similarity network matrix and a brain network module for identifying ischemic stroke neural dysfunction; 1782 sampling points are extracted from the Airy human brain map, and each sampling point comprises expression data of 10185 genes; the method comprises the following steps: mapping space coordinates of AHBA sampling points to a cortex package of a Desikan-Killiany map, carrying out normalization processing to output 308 * 10185 brain region gene-by-gene expression matrixes, and constructing a structural network-gene map biological network model for predicting ischemic stroke by adopting a partial least square regression method and a bootstrap method. Compared with the prior art, the model determines the specific molecular mechanism related to the phenotypic structure change of ischemic stroke injury, and the stroke occurrence probability is predicted according to the specific molecular mechanism.

The application discloses a drug repositioning method and system based on heterogeneous knowledge and structure fusion, and belongs to the technical field of medical care informatics. The method comprises the following steps: constructing a biomedical field knowledge heterogeneous graph; based on the biomedical field knowledge heterogeneous graph, generating disease knowledge embedding and drug knowledge embedding corresponding to a target drug-disease pair; by constructing a drug-drug similarity network, a disease-disease similarity network and a drug-disease association network, generating disease structure embedding and drug structure embedding corresponding to the target drug-disease pair; and based on the disease knowledge embedding, the drug knowledge embedding, the disease structure embedding and the drug structure embedding, obtaining a drug repositioning result. The application accurately captures complex biological network characteristics and finely models complex entity information through an innovative drug repositioning model, so that accurate drug repositioning is realized.

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.

The application provides a forestry carbon sink project full life cycle measurement method and system based on digital twinning, relates to forestry carbon sink measurement, and comprises the following steps: acquiring multi-source heterogeneous basic data such as forest three-dimensional skeleton topology, carbon dioxide flux time sequence, mycorrhizal network distribution and meteorological data, coupling forest digital gene and ecological process tensor to generate a digital gene tensor field; then obtaining a double-path carbon flux field of dominant conduit transmission and implicit mycorrhizal transfer through carbon transmission inversion driven by a conduit bundle network; constructing a carbon sink phase change field based on a stability boundary constraint by analyzing a carbon sink phase change critical point; obtaining a carbon re-allocation map regulated by a biological network through a cross-scale carbon re-allocation path deduction; and finally outputting an optimized and adjusted carbon sink evaluation value through full life cycle carbon sink entropy optimization measurement processing. The application realizes the transformation from static estimation to dynamic simulation of carbon sink measurement, and significantly improves the accuracy and reliability of full life cycle evaluation of a carbon sink project.

This invention relates to the field of bioinformatics technology, specifically a method for mining key multi-omics molecules and pathways based on a heterogeneous graph framework. The steps of this method are as follows: (1) Constructing an integrated biological knowledge graph. (2) Using various machine learning algorithms to convert the original multi-omics data into a multi-dimensional machine learning attribute matrix. (3) Extracting specific multi-layer knowledge networks related to specific diseases from the integrated biological knowledge graph. (4) Matching the preprocessed machine learning attribute matrix with the specific multi-layer knowledge network. (5) Based on the complete network structure, capturing key molecular information using a graph convolutional neural network with an attention mechanism equipped with a purifier. (6) Using a community detection algorithm to extract functional modules from the trained network. This method relies on a biological knowledge graph, interconnects biological networks with experimental data through a heterogeneous graph deep learning model, and combines a graph structure variational autoencoder and an optimized graph convolutional neural network to achieve information optimization and deep mining.

The invention discloses a medicine relocation system and method based on a quantum graph Fourier convolution network. The method comprises the following steps: integrating proteins, genes, microorganisms, metabolites, drugs, diseases and multi-source associated information of the proteins, the genes, the microorganisms, the metabolites, the drugs, the diseases to construct a heterogeneous biological information network and a local subgraph network; the method comprises the following steps: innovatively designing a quantum graph Fourier convolution network, extracting spectrum characteristics and topological information of protein, gene, microorganism, metabolite, medicine and disease nodes from a heterogeneous biological information network, updating medicine and disease node information, and obtaining discriminative characteristic representation; a multi-layer sensor is used for learning and depicting medicine-disease joint feature representation, and then the unknown curative effect of the medicine is predicted. According to the method, data mining and knowledge discovery are carried out based on the multi-source heterogeneous biological information network, the quantum Fourier graph convolutional network is innovatively used for modeling the multi-source heterogeneous biological network, high-order semantic dependence and a global structure mode between drugs and diseases can be effectively extracted, the performance of a drug relocation prediction system is improved, and the system is suitable for large-scale popularization and application. And the method has good expandability and practical application prospects.

A method for developing novel drug therapies utilizing properties of existing drug therapies, the method comprising the steps of: developing a novel therapeutic mathematical model of a target biological network and synthesizing a drug therapy based on the novel therapeutic mathematical model. The novel therapeutic mathematical model is capable of generating novel therapeutic time-course progressions, which include time-course progression features found in existing therapeutic time-course progressions of existing therapeutic mathematical models of the target biological network. The time-course progression features can be correlated with the outcomes of the target biological network. Each new therapeutic intervention constant can be used to synthesize a drug regimen, and these drug regimens collectively constitute the novel drug therapy.

The application discloses a cancer molecular marker prediction method based on multi-omics and intelligent feature mining, and belongs to the technical field of bioinformatics and artificial intelligence; the method comprises the following steps: collecting multi-omics data of cancer samples, including genomic, transcriptomic, epigenetic, proteomic and metabolomic data; performing quality control and pretreatment on the data; performing intelligent feature mining by adopting difference analysis, dimension reduction algorithm and feature fusion network; integrating biological networks to construct a multi-view graph neural network prediction model; identifying key molecular markers and performing verification; the application can effectively integrate multi-level omics data, mine molecular markers with biological significance, and establish a high-precision and high-explainability prediction model; the application provides an effective technical means for early diagnosis, molecular typing, prognosis evaluation and individualized treatment of cancer, and has important scientific significance and clinical application value.

The present application belongs to the technical field of bioinformatics, and specifically relates to a computer simulation and regulation method and system for beef cattle fat metabolism, aiming to solve the problems of excessive fat deposition, low feed conversion efficiency and multi-objective optimization difficulty in existing beef cattle breeding, by establishing a biological data acquisition system, constructing a multi-scale biological network model, developing a computer simulation and prediction engine, designing an intelligent intervention strategy optimization engine, and implementing and feedback optimization. By reducing unnecessary fat deposition, improving feed conversion efficiency and improving beef quality, the present application not only significantly improves the economic benefit of beef cattle breeding, but also responds to the demand of consumers for high-quality and safe beef products, and has important value for promoting the intelligent upgrading and sustainable development of the beef cattle industry.

The invention relates to the technical field of biological medicine, and discloses a pulmonary fibrosis drug target screening method fusing multi-omics data. The method comprises the following steps: acquiring multi-omics original data related to pulmonary fibrosis from a public database and an experimental data source; performing quality control and normalization processing on the data to generate a standardized multi-omics data set; integrating the data set into a unified multi-omics feature expression spectrum through a data fusion technology; analyzing the expression profile by using a calculation model based on a biological network, and deducing the correlation degree between each potential target spot and the pulmonary fibrosis pathological process; sorting the potential target spots according to the correlation degree, and screening out high-priority drug target spots; and importing the high-priority drug targets into a drug target management system for persistent storage. According to the system, multiple omics data are integrated through the system, the relevance between the targets and diseases is quantified, accurate and efficient screening of the pulmonary fibrosis drug targets is achieved, and reliable data support is provided for research and development of new drugs.

The invention discloses an AI-driven traditional Chinese medicine-glioma target point synergistic effect network prediction system, which comprises a multi-mode biomedical data acquisition and preprocessing module, an AI-driven traditional Chinese medicine-glioma target point network prediction module and a target point prediction module, the data acquisition module is used for acquiring heterogeneous biomedical data associated with traditional Chinese medicine components and biological activity thereof, glioma related targets and molecular characteristics thereof, and traditional Chinese medicine-target interaction from a plurality of data sources; and the multi-level biological network construction module is electrically connected with the multi-modal biomedical data acquisition and preprocessing module. The invention relates to the technical field of bioinformatics. According to the AI-driven traditional Chinese medicine-glioma target point synergistic effect network prediction system, massive heterogeneous biomedical data are effectively integrated and standardized through the multi-mode biomedical data acquisition and preprocessing module, the defects that a data source is single and complex association is difficult to capture in a traditional method are overcome, and the multi-level biological network construction module has the advantages of high efficiency, high reliability and the like. A multi-dimensional and multi-scale biological network is constructed from the perspective of system biology.

An information handling system may include at least one processor; and a wireless network interface adapter; wherein the information handling system is configured to: store credentials for a wireless network in a secure storage vault accessible from a pre-boot environment; and during execution of the pre-boot environment, connect the wireless network interface adapter to the wireless network based on the credentials without requiring user input of the credentials.

The present disclosure relates to the field of computer technology, and provides a traditional Chinese and Western medicine comprehensive intervention decision system, comprising a data acquisition module; a diagnosis reasoning module, configured to obtain analyzed traditional Chinese medicine syndrome information and analyzed Western medicine disease information according to traditional Chinese medicine and analyzed Western medicine physiological state data, and obtain traditional Chinese and Western medicine comprehensive diagnosis information; a multi-omics mapping module, configured to map pathological mechanism information according to the analyzed traditional Chinese medicine syndrome information; a fusion network module, configured to construct / call a fusion biological network corresponding to each preset traditional Chinese and Western medicine comprehensive diagnosis information; and an intervention decision module, configured to determine a recommended traditional Chinese and Western medicine comprehensive intervention scheme in an intervention scheme group associated with a target key target point / channel based on a prediction of a traditional Chinese and Western medicine collaborative intervention effect, and output the recommended traditional Chinese and Western medicine comprehensive intervention scheme. The present disclosure realizes the feature mapping and fusion of a Western medicine micro model and a traditional Chinese medicine macro model, so that a precise traditional Chinese and Western medicine comprehensive intervention scheme recommendation can be obtained, and the problems in the related art are effectively solved.

This invention discloses a key protein identification method based on two-stream supergraphs and multi-level gating in the field of multi-source bioinformatics. The method includes the following steps: S1, acquiring the target PPI network and multi-source biological heterogeneous attribute data, extracting network interaction edge data of the target protein, and constructing a basic PPI network view; S2, based on the PPI network view constructed in step 1, performing high-order topological information extraction based on local closed-loop subgraphs, thereby overcoming topological noise interference caused by inherent false positive edges in the basic interactive network, and deeply mining the synergistic interaction patterns of macromolecular complexes in spatial conformation. The algorithm of this invention exhibits good performance advantages in key protein identification, demonstrating that the dynamic fusion effect of high-order topology and multi-dimensional attributes is superior to single feature or homogeneous splicing strategies, providing a new approach and algorithm for solving the problem of accurate screening of key targets in complex biological networks.

The invention discloses a hereditary disease candidate gene sorting method and device based on heterogeneous graph embedding, and relates to the field of biological information. The method comprises the following steps: constructing a phenotype-gene heterogeneous network, and determining an edge weight in the heterogeneous network according to an association frequency of genes and phenotypes in a clinical medical record; capturing heterogeneous neighbor nodes based on meta-path weighted random walk according to the types of the neighbor nodes, and obtaining node embedding in the heterogeneous network; and according to the node embedding corresponding to the phenotypic node and the node embedding corresponding to the gene node, evaluating the possibility that the candidate gene is a pathogenic gene, and according to an evaluation result, sorting the priority of the candidate gene. Through the method, heterogeneous information in a biological network is effectively captured, the priority ranking accuracy of candidate genes is improved, the historical medical record data is introduced to generate the edge weight, and the expression ability of a heterogeneous graph and the credibility of a virulence gene prediction result are improved.

The application discloses a comprehensive computing platform based on CRISPR gene dependence map and application thereof, and the platform comprises: an Essentiality Analysis module for calculating gene dependence difference among cell lines or tissues, and supporting multi-modal joint browsing of dependence scores, expression levels, CNV and mutation states; an Essentiality2Target module for hierarchical analysis of gene dependence based on cancer molecular characteristics to screen candidate therapeutic targets in specific backgrounds; an Essentiality2Drug module for generating a candidate therapeutic drug list for a specified gene or background by using drug perturbation data, drug target information and gene dependence characteristics; and a DIY Tools module for displaying gene correlation and gene dependence spectrum in an interactive visual manner. The application connects the platform of CRISPR induced gene dependence with drug response and biological network, and establishes an open access example for precision oncology research on non-druggable targets.

The invention provides a personalized graph federated learning method and system based on attention and structure perception, and relates to the technical field of federated learning and graph data processing. The method aims at core pain points of cross-client side missing, data heterogeneity and insufficient global semantic capture in federal sub-graph scenes such as cross-institution scientific research cooperation, e-commerce platform cooperation and biological network conjoint analysis. A four-step architecture of three-vision attention semantic extraction, structure perception supplement, clustering prototype comparison and personalized aggregation is adopted, three-vision attention is guided through a neighbor center, global sparsity and a prototype, local association, global long-distance dependence and category semantic anchor points are captured respectively, topological structure features are supplemented by using a graph convolutional network, and the three-vision attention is extracted. Data heterogeneity is relieved based on clustering-prototype federation contrast learning, and personalized parameter aggregation is realized through prototype similarity. The method is suitable for practical tasks such as document classification, commodity recommendation and protein function prediction.

The invention discloses a microorganism and drug relationship prediction method and system based on quantum graph convolution calculation. The method comprises the following steps: firstly, integrating multiple omics data to construct a heterogeneous biological information network associated with microorganisms and drugs; secondly, topological structures and semantic information of microorganisms and drug nodes are extracted based on a quantum graph convolutional network, and feature representations of drugs and microorganisms are obtained; and finally, predicting the relationship between the microorganisms and the drugs by using a Bayesian classifier. According to the method, microorganism and drug data modeling and node feature learning and fusion are performed based on a multi-source heterogeneous biological information network, and a graph convolutional network and quantum machine learning are combined; a correlation analysis mechanism among diseases, genes, metabolites, drugs and microorganisms in the heterogeneous biological information network is accurately and deeply understood, and an interaction relationship between the drugs and the microorganisms is predicted; the method has good expandability, practicability and application prospects in the field of complex biological network information mining of artificial intelligence, biomedicine and system science.

The invention discloses a non-coding RNA-disease association prediction method, device and equipment and a storage medium, and is applied to the technical field of biological information, and the method comprises the steps: constructing a heterogeneous biological network, employing a graph neural network to learn the distributed representation of a biological entity from a network topology structure and an entity attribute feature based on the heterogeneous biological network, and obtaining the distributed representation of the biological entity; different view information is fused through an attention mechanism; establishing a multi-task learning model, and extracting a shared biological mode suitable for all prediction tasks from the graph neural network; generating prediction results of different association types through a decoder based on the shared biological mode; according to the method, multi-task training is optimized through reinforcement learning dynamic parameter adjustment, heterogeneous and attribute information is fused through a double-view graph network, a multi-task framework is unified to share a biological mode, and special decoding is carried out, so that collaborative improvement of precision and generalization ability in ncRNAs-disease association prediction is realized.

This invention relates to a disease similarity prediction method and system based on multivariate data fusion. The method includes: collecting gene network data, miRNA network data, disease-gene association matrices, and disease-miRNA association matrices to form a unified heterogeneous biological network; extracting a multimodal feature set through graph convolutional networks and improved graph attention networks; calculating the independent weights of each feature in the multimodal feature set using a multilayer perceptron, and combining the association matrices to generate disease-specific gene-perspective embeddings and miRNA-perspective embeddings, which are used as model inputs to train a lightweight bilinear tower model; optimizing the model parameters through an alternating training strategy and a ReconBoost-inspired regularization mechanism to obtain an optimized lightweight bilinear tower model, and obtaining the similarity prediction results for target disease pairs. This effectively overcomes the shortcomings of existing technologies in multivariate data integration, sparse data processing, and prediction stability.

This invention discloses a method for screening optimal metabolite feature combinations for non-targeted metabolomics data. By injecting the topological robustness score (TRS) and prior weights obtained in step S1 into the dynamic fitness function calculation in the genetic algorithm screening process in step S2, it is ensured that the selected optimal metabolite feature combinations not only have high classification efficiency, but also are all located in the core functional modules of the biological network, reducing the interference of spurious markers. Therefore, this invention can significantly improve the biological interpretability of metabolite biomarker combination screening while meeting the constraint of the ideal number of selected metabolite features.

The application belongs to the technical field of drug target relationship prediction, and relates to a drug and target interaction prediction method and device and a storage medium; based on drugs, targets, diseases, side effects and their interactions, a heterogeneous biological network graph is constructed to obtain initial feature vectors of each node; the drug node and the target node are taken as target nodes to obtain four types of neighbor nodes and four types of meta-path nodes of the target nodes; the initial feature vectors of the four types of neighbor nodes and the four types of meta-path nodes of the target nodes are subjected to feature extraction to obtain neighbor view embedding representation and meta-path view embedding representation; the neighbor view embedding representation and the meta-path view embedding representation are projected into query vectors, key vectors and value vectors, and cross-attention values are calculated; based on the neighbor view embedding representation, the meta-path view embedding representation and the cross-attention values of each target node, a fusion representation is obtained; and based on the fusion representation, interaction prediction of the drug node and the target node is carried out.

The invention provides an iron ore tailing heavy metal chromium long-acting solid storage restoration method based on a dynamic regulation and control rhizosphere microorganism network, and relates to the technical field of environmental data processing. And a Cr long-acting solid storage prediction model and a dynamic rhizosphere microorganism network model are used for decision making and regulation and control. The method comprises the specific steps of data extraction and fusion, microorganism application and feedback data acquisition, and finally updating a model to reflect the states of microorganisms and heavy metal Cr, so that effective solid storage and management of the heavy metal Cr in the iron ore tailings are realized, and the ecological restoration of mines and the safety of ecological environments are promoted.

The application provides a simplicial complex-based random high-order network pinning control method and relates to the technical fields of control and information. The application is directed to a simplicial complex network affected by noise described by a nonlinear stochastic differential equation, and a reasonable and effective pinning control law is designed to achieve the synchronization target. By introducing a simplicial complex framework, the model can accurately describe the high-order correlation and complex interaction relationship between nodes in the network, and is more close to the characteristics of the actual system. The description method of the nonlinear Ito type stochastic differential equation is adopted, and the network model can truly reproduce the nonlinear characteristics of the actual system and the influence of the modeling noise. On this basis, the pinning control strategy designed has a wider application range, a lower control cost, can enhance the stability of the network, and can avoid the system from falling into instability or functional degradation, thereby providing strong theoretical support and technical tools for the regulation and control of the dynamic behavior of complex systems such as biological networks, social networks and communication networks.

The application discloses a network pharmacology complex biological relation prediction model construction method based on deep learning, which comprises the following steps: step one, multi-source heterogeneous data integration, collecting and cleaning multi-source heterogeneous data, and constructing a heterogeneous biological network; step two, multi-modal feature extraction, aiming at different nodes and edge types in the heterogeneous network; step three, network model construction, constructing a dynamic heterogeneous graph neural network model based on the fusion features; step four, multi-task learning and model optimization, jointly optimizing multiple related tasks through multi-task learning; step five, model verification, verifying the model performance through wet experiments and clinical data, and the key drug-target pairs predicted by the model; the application integrates multi-source heterogeneous data, extracts molecular topological, network topological and text semantic features by using a graph neural network and a cross-modal attention mechanism, constructs a dynamic heterogeneous graph neural network model, and realizes high-precision prediction and mechanism analysis of drug-target-disease correlation.