54 results about "Biological pathway" patented technology

The invention relates to the technical field of knowledge maps, in particular to a multi-task hepatitis B drug screening method and system based on knowledge map assistance, and the method comprises the following steps: based on hepatitis B virus genotype sequence data recorded along with time, patient drug use records and drug sensitivity. According to the method, virus genotype sequence data, patient drug use records, drug sensitivity and other multi-dimensional dynamic time sequence data are integrated, interaction events between entities are marked through timestamps, and virus variation tracks, drug curative effect changes and other dynamic characteristics are embedded into atlas node attributes; and the knowledge representation can reflect the time dependence in a real scene. A hyperedge connection multi-entity set is defined based on biological pathway annotation information and protein interaction data, the limitation that only a binary relation is supported in a traditional knowledge graph is expanded, a drug combination and multi-target synergistic effect mechanism is explicitly modeled, and misjudgment of the combination effect caused by simplification of the interaction relation is avoided.

The invention provides a method for detecting genes related to early growth traits of Inner Mongolia down producing goats based on copy number variation (CNV). The method comprises the following steps: acquiring whole genome re-sequencing data of 461 Inner Mongolia down producing goats; the method comprises the following steps: detecting autosomal CNV by using CNV native software, and screening out 26,003 non-redundant CNV which covers 1, 015.4 Mb (38.97%) of an autosomal genome; gWAS analysis is carried out on the basis of CNV, 11 CNVs obviously related to early growth traits (birth weight CSZ, weaning weight DRZ, daily gain ADG before weaning and 12-month-old weight ZWT) are identified, and two CNVs influence ADG and DRZ at the same time; seven key candidate genes (ZN845, SOX15, FGF11, GPS2, DVL2, SPRY4 and STAT2) are analyzed and identified through bioinformatics, and GO and KEGG function enrichment analysis is utilized to find that the genes participate in biological pathways such as cell proliferation, differentiation and protein phosphorylation. The invention provides a theoretical basis for molecular marker-assisted breeding of Inner Mongolia down producing goats, and has important scientific value and application prospect.

The present application relates to the technical field of knowledge graph, in particular to a multi-task hepatitis B drug screening method and system based on knowledge graph assistance, comprising the following steps: based on hepatitis B virus genotype sequence data recorded over time, patient drug use records and drug sensitivity. In the present application, multi-dimensional dynamic time series data such as virus genotype sequence data, patient drug use records and drug sensitivity are integrated, the interaction events between entities are marked by time stamp, the dynamic characteristics such as virus variation track and drug efficacy change are embedded in the graph node attributes, so that the knowledge representation can reflect the time dependence in the real scene. Based on biological pathway annotation information and protein interaction data, the hyperedge connection multi-entity set is defined, the limitation of traditional knowledge graph which only supports binary relationship is expanded, the drug combination and multi-target synergistic mechanism are explicitly modeled, and the misjudgment of combination effect caused by the simplification of interaction relationship is avoided.

A method, apparatus and computer software product are provided for identifying a pathway-affecting molecule for affecting a biological pathway. A computer model generates an initial list including one or more molecules and revises the list until the list includes the pathway- affecting molecule, by repeatedly: applying each of the molecules in the list to a biological sample, such that the molecule interacts with the biological sample, identifying one or more effects of the interaction, and inputting, to the computer model, the effects of the interaction, such that the computer model revises the list based on the effects. Based on the effects of the interaction of the pathway-affecting molecule with the biological sample, the pathway-affecting molecule is identified. Other embodiments are also described.

The invention provides glycosyl transferase and application thereof in preparation of salidroside, and relates to the technical field of enzyme engineering. The glycosyl transferase FpUGT provided by the invention is derived from fraxinus pennsylvanica, the existence of the glycosyl transferase gene capable of directly catalytically synthesizing the salidroside in the plant is not reported in literatures, and a new biological way is provided for finding the glycosyl transferase for efficiently synthesizing the salidroside. And a new biological material is provided for preparing the salidroside.

The application relates to a cell infiltration inference method and system fusing GO function annotation and PPI network information, and the method comprises the following steps: collecting gene expression data, GO function annotation data and PPI network data; constructing a cell-cell function correlation network and a cell-cell physical interaction network respectively; performing weighted fusion processing on the two networks to obtain a comprehensive cell relationship network; calculating a final cell infiltration score through a restart walk algorithm, and inferring the infiltration degree in a tumor microenvironment according to the final cell infiltration score. The application innovatively fuses GO function annotation information and PPI network data, comprehensively considers the functional similarity and physical or signal interaction between cells, enables the model to understand cell synergy from the biological pathway level and analyze cell direct interaction from the protein interaction level, avoids one-sidedness of a single perspective, and provides a more stereoscopic cognitive framework for tumor microenvironment analysis.

The invention belongs to the field of plant engineering technology and crop germplasm resource innovation, and particularly relates to a rice BEIIb gene mutant, application of the rice BEIIb gene mutant in improving rice resistant starch and an improving method. A mutant material with high resistant starch content is created by knocking out the rice BEIIb gene, and compared with a wild type, mutant seeds show an obvious chalky phenotype, composite starch particles are abnormal in a spherical or ellipsoidal shape, the amylose content is remarkably increased, the protein content is remarkably reduced, the resistant starch content is remarkably increased, and the resistant starch content is remarkably reduced. The hardness of cooked rice is remarkably increased, and the elasticity is remarkably reduced. Through transcriptomics analysis, the protein mainly participates in biological pathways such as carbohydrate metabolism, chloroplast photosynthesis, calmodulin combination and secondary metabolite synthesis, and the expression level of endosperm starch synthesis related genes is obviously changed. The method can provide theoretical basis and method reference for breeding of high-quality rice varieties with high resistant starch.

The invention relates to a cell infiltration inference method and system fusing GO function annotation and PPI network information. The method comprises the following steps: collecting gene expression data, GO function annotation data and PPI network data; respectively constructing a cell * cell function association network and a cell * cell physical interaction network; carrying out weighted fusion processing on the two to obtain a comprehensive cell relation network; and calculating a final cell infiltration fraction through a restart migration algorithm, and deducing the infiltration degree in the tumor microenvironment according to the final cell infiltration fraction. According to the method, GO function annotation information and PPI network data are creatively fused, functional similarity and physical or signal interaction between cells are comprehensively considered, the model can understand cell synergy from the biological pathway level and can analyze cell direct interaction from the protein interaction level, one-sidedness of a single view angle is avoided, and the method has the advantages of being simple in structure and convenient to operate. And a more three-dimensional cognitive framework is provided for tumor microenvironment analysis.

The invention discloses a method and device suitable for tumor glucose metabolism reprogramming driving factor analysis, and the method comprises the steps: generating a gene expression matrix for a biological pathway under a sample; performing dimension reduction processing on the gene expression matrix to obtain a first low-dimensional representation and a second low-dimensional representation; inputting the first low-dimensional representation and the second low-dimensional representation into a causal direction inference structure, and outputting a plurality of initial causal direction inference results; selecting from the plurality of initial causal direction inference results to obtain a target causal direction inference result corresponding to the sample; and finally, analyzing and comparing a target causal direction inference result under a normal sample and a target causal direction inference result under a tumor sample to obtain an analysis result, and indicating that Fenton reaction in cells drives tumor glucose metabolism reprogramming. Therefore, key factors of tumor glucose metabolism reprogramming can be quickly and accurately identified, and the fact that the Fenton reaction in cells is a main reason of tumor glucose glycolipid metabolism reprogramming is found.

The present disclosure, in certain aspects, is directed to the use of causal networks for aspects of biological study and drug discovery, including the identification of biological pathways, or components thereof, associated with a stimulus and / or condition, identification of a drug useful for treating and / or preventing a condition, drug target prioritization, and drug repurposing.

The application provides a kind of genotypes and phenotypic association analysis method based on multiple phenotypes, belongs to the technical field of bioinformatics, and establishes SNP-gene-phenotype three-layer network to explore the relationship between genotypes and phenotypes using statistical data, and the internal correlation between genomics and genomics, such as genes and genes, and phenotypes and phenotypes, is considered in the three-layer network construction process, which is more consistent with biological reality;Solve the problem that clinical data is difficult to obtain and SNP and phenotype correlation cannot be predicted;The biological pathway correlation between SNP-gene-phenotype different omics layers is analyzed.Through the analysis of various database data, it is found that the internal correlation of each database omics has quantitative value, while the correlation between omics generally has qualitative value, i.e. the correlation is 1 and the non-correlation is 0.Through the establishment of three-layer model, the key correlation genes can be predicted using the quantitative value of the internal omics and the qualitative value of the inter-omeric relationship, and the pathway relationship score between each layer is calculated to analyze the pathway relationship between SNP-gene-phenotype.

The application provides a glycosyltransferase and its application in preparing rhodiolin, and relates to the technical field of enzyme engineering. Fraxinus pennsylvanica The glycosyltransferase FpUGT in the application is derived from Malpighia emarginata DC. There is no report about the glycosyltransferase gene capable of directly catalyzing synthesis of rhodiolin in the plants of the genus Malpighia, the application provides a new biological route for searching for the glycosyltransferase with high efficiency in synthesizing rhodiolin, and also provides a new biological material for preparing rhodiolin.

The invention discloses a muscle fiber specialization core target point mining method and system based on multi-modal learning, particularly relates to the technical field of poultry muscle biology research, and is used for solving the problem of target point mining reliability caused by insufficient space-time alignment precision of multi-modal data in existing living body detection. The method comprises the following steps: acquiring living goose muscle tissue imaging and molecular omics data, performing spatial registration and time sequence alignment processing to generate time-space synchronous multi-modal data, calculating a time sequence correlation coefficient, judging whether the time sequence correlation coefficient exceeds a threshold value or not, generating a biological pathway activation intensity attention map based on the molecular omics data, and performing feature fusion. And finally, mining a core target spot through the target spot identification model and outputting a list, thereby realizing accurate identification of the muscle fiber specialization key target spot in a living body state.

The invention relates to the technical field of information retrieval, and provides a pharmacological analysis system for traditional Chinese medicine prescriptions, the system comprises a multi-omics feature fusion subsystem, a multi-omics data set is subjected to feature extraction and dimension reduction operation, and a fused multi-omics feature vector is obtained; the multi-omics feature vector captures the comprehensive influence of the traditional Chinese medicine prescription on genome, proteome and metabolome levels; performing clustering and community detection on a component-target dynamic interaction network of the component collaborative network construction subsystem to obtain a component collaborative sub-network; a prescription-disease association map of the disease association path discovery subsystem is subjected to path mining to obtain a potential disease association path set; a novel prescription-disease association path is obtained; and the traditional Chinese medicine prescription optimization candidate schemes of the prescription optimization decision generation subsystem are subjected to multi-objective optimization to obtain a traditional Chinese medicine prescription optimization decision set. According to the invention, full-chain correlation analysis of chemical components, action targets, biological pathways and disease treatment of traditional Chinese medicine prescriptions is realized.

This application provides a method, apparatus, electronic device, and storage medium for risk assessment of compounds, relating to the field of wastewater treatment technology. The method includes: identifying multiple compounds in a target type of wastewater and identifying first target proteins corresponding to the multiple compounds; wherein the first target protein is a human target protein for the compounds; determining health risk scores corresponding to the multiple compounds based on at least one of a score indicating the degree of influence of the multiple compounds in a pathway network and a score indicating the binding strength between the multiple compounds and their corresponding first target proteins; wherein the pathway network is used to characterize the correspondence between the compounds, the target genes of the first target proteins, and biological pathways, and the pathway network is constructed through enrichment analysis of the target genes. This application can accurately quantify the impact of compounds in wastewater on human health, improving the accuracy of compound risk assessment.

A method, apparatus and computer software product are provided for identifying a pathway-affecting molecule for affecting a biological pathway. A computer model generates an initial list including one or more molecules and revises the list until the list includes the pathway- affecting molecule, by repeatedly: applying each of the molecules in the list to a biological sample, such that the molecule interacts with the biological sample, identifying one or more effects of the interaction, and inputting, to the computer model, the effects of the interaction, such that the computer model revises the list based on the effects. Based on the effects of the interaction of the pathway-affecting molecule with the biological sample, the pathway-affecting molecule is identified. Other embodiments are also described.

The present disclosure relates to Sigma-1 receptor agonists, and their target engagement and capacity to correct multiple EEG biomarkers of cortical dysfunction. The present disclosure further provides Precision Medicine or personalized therapy of using a Sigma-1 receptor agonist in treating neurodevelopmental and / or neurodegenerative disorder through engaging with EEG biological pathways. Also provided are kits for practicing the methods.

The application discloses a method for predicting active structures interfering with biological pathways, which comprises the following steps: constructing a compound biological pathway interference database containing clear labels such as cell lines, exposure time and exposure concentration; evaluating the consistency of the biological pathway cross degree and the regulation trend of the training set and the test set through cumulative hypergeometric distribution and cumulative Bernoulli distribution; identifying a batch of potential compounds in the training set; evaluating the occurrence frequency of the molecular descriptors in the potential compounds through cumulative distribution probability; and finally predicting the potential active structures driving the change of the biological pathway through inputting the biological pathway.

The invention relates to the technical field of screening of effective components of traditional Chinese medicine compound preparations, and discloses a deep learning-based method for screening effective parts of compound mengbrand colon sustained-release tablets, which comprises the following steps of: constructing a time sequence pharmacodynamic pathway library, and recording biological pathway activity data of each component of the compound mengbrand colon sustained-release tablets at a plurality of time points; designing a causal intervention traceability network, and analyzing and quantifying the causal relationship of each component to a path node through anti-factual intervention; developing a key node identification algorithm, and identifying key regulation and control nodes; establishing a pathway evolution tracking model, and predicting the long-term influence of the drug components on the pathway; generating a mechanism interpretability score, and evaluating the pharmacodynamic mechanism reliability of each component; effective components with a definite action mechanism are screened out; according to the method, by constructing the time sequence pharmacodynamic pathway library, the complete change process of the biological pathway at multiple time points after drug intervention can be recorded, and the limitation that a traditional static end point observation method cannot track a complete causal chain is overcome.

The application discloses a breast cancer patient axillary lymph node metastasis state prediction method based on biological prior knowledge, and relates to the fields of artificial intelligence, bioinformatics and medical data analysis technology and the like. The technical process of the application comprises multi-source heterogeneous data acquisition, multi-source biological data processing, biological pathway similar weighted matrix construction, biological pathway feature map construction and hierarchical graph neural network. The application introduces biological functional pathways as a structured intermediate layer, maps patient individual expression signals to a unified functional topological space, constructs a patient-specific pathway feature map expression framework that can be used for deep learning, and builds a targeted hierarchical graph neural network for the patient-specific pathway feature map for feature extraction. While maintaining biological interpretability, the breast cancer patient transcriptomic expression data heterogeneity problem is effectively resolved, the traditional "gene as feature" flat modeling mode is broken through, and the application is suitable for scientific research and medical clinical auxiliary diagnosis application scenarios such as significant biological pathway screening, disease metastasis risk stratification and prognosis evaluation.

The application provides a pathway-embedded traditional Chinese medicine efficacy substance synergistic effect prediction method and system, and belongs to the technical field of traditional Chinese medicine efficacy substance synergistic effect prediction. First, the drug molecular structure data is preprocessed to generate a drug molecule representation vector; then, based on the target database information and the drug molecule representation vector, a drug and each target association relationship is constructed; then, the collected target and biological pathway relationship data are used to generate an adjacency matrix of multiple target pathways, the adjacency matrix is fused with the obtained drug and each target association relationship to obtain each target embedding feature; the target embedding feature is used for deep learning training to generate a drug synergistic effect prediction model; and finally, the synergistic effect prediction result of the drug combination is intuitively presented. The application fuses the pathway embedding technology, the graph convolutional neural network, the deep Q network algorithm and the like, constructs a more accurate and efficient efficacy prediction model, and realizes accurate, efficient and comprehensive prediction of the synergistic effect of traditional Chinese medicine efficacy substances.

The invention discloses a fecal incontinence animal model making system. A gradient damping type electrical stimulation parameter regulation and control unit comprises a variable slope driving cam and a sliding block, and the peripheral surface of the variable slope driving cam is divided into an activation section, a maintenance section, an attenuation section and a reset section according to a stimulation period; one end of the sliding block abuts against the variable-slope driving cam, the other end of the sliding block is connected with the electrical stimulation parameter adjusting element, and the variable-slope driving cam rotates to drive the sliding block to move linearly to achieve gradient changes of electrical stimulation frequency and intensity. The three-stage locking type modeling unit realizes closed-loop operation and stable control of a modeling process; the bidirectional buffer type flexible stimulation unit is electrically connected with the gradient damping type electrical stimulation parameter regulation and control unit and is in direct contact with biological tissue, and mechanical damage of a probe to nervous tissue is avoided through a multi-layer buffer structure. Gradient dynamic adjustment of electrical stimulation parameters is achieved, accuracy and stability are achieved, electromagnetic interference is avoided, the dynamic activation process of a biological pathway is simulated, and a standardized experiment platform is provided for preclinical treatment scheme research.

This invention discloses a method and system for predicting drug synergy based on cellular semantic-guided dynamic biological pathway weights. This method integrates semantic priors from a large language model, dynamic biological pathway gating, and a triplet self-attention mechanism. It dynamically regulates gene-pathway mapping relationships through cellular semantic features to form soft constraints, and applies biological hard constraints to network weights through a binary mask matrix constructed from gene-pathway subordination relationships. The combination of these two approaches, while adhering to the underlying facts of molecular biology, introduces semantic soft gating to adapt to the heterogeneity of the cellular microenvironment. On this basis, the complex interactions between drugs and cells are captured through a sequenced triplet self-attention mechanism, achieving accurate identification of drug synergy and deep coupling of cross-modal information, thereby improving the biological interpretability and generalization ability of the prediction.

The invention relates to an accurate medication time recommendation method and system based on a multi-organization rhythm map. The method comprises the following steps: constructing a drug database and a rhythm expression database; acquiring a drug identifier input by a user, performing retrieval in a database, performing rhythm feature analysis based on a retrieval result, and generating a drug-target rhythm analysis report; based on the drug information queried by the two-stage serial query architecture, querying all biological pathways and related genes in which the drug participates, carrying out standardized conversion on gene identifiers, carrying out rhythm feature analysis, and generating a drug-pathway rhythm analysis report; and taking the generated analysis report as input, carrying out semantic integration and reasoning through a locally deployed large language model, and automatically generating an optimal medication time suggestion. Compared with the prior art, the method has the advantages that batch rhythm characteristic analysis of drug target genes and related pathway genes is realized, the analysis efficiency and repeatability are greatly improved, and the decision intelligence level is improved.

The invention discloses an SLC protein-disease association prediction method based on a knowledge graph. The method comprises the following steps: 1) constructing an SLC protein-disease knowledge graph based on SLC protein and disease related data; 2) analyzing and mining the knowledge graph by using a convolutional neural network model to obtain the potential association of the SLC protein and the disease in different biological pathways, and the method comprises the following steps: taking two nodes without connecting edges in the knowledge graph as to-be-processed objects; performing graph structure coding on the knowledge graph, and generating embedded vectors of corresponding nodes according to semantic features and structural features of the nodes; the semantic features are text vector representations of the nodes, and the structural features are adjacency relations and path information of the nodes; and inputting the embedded vectors of the two nodes corresponding to the to-be-processed object into the relation scoring function to obtain a potential relation score of the to-be-processed object, and determining potential association existing in different biological pathways of the two nodes corresponding to the to-be-processed object according to a scoring result.

This application relates to the field of bioinformatics, and specifically relates to a method and system for constructing a disease-specific omics database. The method includes obtaining gene expression data and gene IDs of a disease sample set; mapping the gene expression data to the biological pathway level to obtain pathway data and pathway IDs; mapping the gene expression data to the immune cell level to obtain immune cell data and immune cell IDs; associating the pathway IDs, the immune cell IDs with the gene IDs to obtain an association relationship; and storing the association relationship, gene expression data, pathway data, and immune cell data to obtain a disease-specific omics database. The database constructed by this method contains data on genes, pathways, and immune cells, and can obtain data on a certain disease in multiple dimensions simultaneously, having good clinical value.

The application provides a method for screening soybean Cercospora sojina blight response genes based on transcriptome analysis, which comprises the following steps: selecting pods of soybean Cercospora sojina blight infected plants and healthy plants planted in the field, performing transcriptome sequencing, screening differential expression genes of soybean responding to Cercospora sojina blight, analyzing biological pathways of the differential expression genes in the Cercospora sojina blight infection process, performing protein interaction analysis, screening interaction core genes, comparing sequences, performing transcript from scratch assembly, analyzing differential variable splicing events and exon usage differences through integration analysis, analyzing post-transcriptional regulation network dynamic characteristics of key candidate genes of soybean responding to Cercospora sojina blight, and screening Cercospora sojina blight response genes of soybean. Four Cercospora sojina blight response genes of soybean are obtained, and expression characteristics in different soybean tissues are analyzed, which provides a theoretical basis for soybean Cercospora sojina blight resistance breeding.

Orally-ingestible supplement formulations for boosting Nicotinamide adenine dinucleotide (NAD+) in a human. The formulations provide a triple boost for NAD+ that will provide a) NAD+ boosters to increase natural production, b) a source of NAD+ and c) inhibitors to keep NAD+ from being metabolized. The formulations have one of each of: 1) an NAD precursor and / or reduced NAD precursor, 2) an NAD booster, 3) a CD38 Inhibitor, and 4) Reduced NAD precursors.