27 results about "Biological pathway" patented technology

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.

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 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 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.

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.

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.

The invention discloses a high-throughput screening system of an hPirin protease inhibitor and application, and relates to the technical field of biomedical engineering.The high-throughput screening system comprises a target spot structure modeling and screening system construction module; a high-throughput virtual screening module; an in-vitro enzyme activity verification module; an enzyme kinetics characterization module; a cell biological activity verification module; the non-specific limitation of a traditional transcription inhibitor is broken through, the enzyme activity center of hPirin protease is screened specially, the enzyme catalysis function can be specifically blocked, interference to other biological pathways is avoided, and the effect specificity of the inhibitor is remarkably improved. The screening system is high in reliability, through the layer-by-layer progressive design of multi-round progressive virtual screening combined with multi-technology combined verification (ultraviolet spectrum + HPLC-MS / MS + enzyme kinetics + cell experiment), the false positive screening result is effectively reduced, and the screening efficiency and quality of the high-activity candidate inhibitor are greatly improved.

The application provides a miscarriage risk assessment method and system of whole-exome load and serum markers, comprising: independently scoring each variation in whole-exome sequencing data to obtain a comprehensive pathogenic score; constructing a gene pathogenic load matrix; mapping the gene pathogenic load to a biological pathway to obtain a pathway comprehensive score and construct a model; solving the model by jointly optimizing the regularization strength parameters of the two-layer ridge penalty terms, outputting significant pathway features; standardizing and preprocessing serum detection data; jointly modeling the output significant pathway features and the output standardized serum data under the same logistic regression probability framework, outputting a miscarriage-related risk probability value, and performing probability stratification. The application also outputs a double-track coordination coefficient to quantify the directional consistency of genetic signals and serum signals. It is significantly superior to a single omics solution and provides a high-precision individualized risk assessment tool for recurrent miscarriage with normal karyotype.

This application relates to the field of bioinformatics computing technology, and discloses a method, device, computer equipment, and storage medium for predicting pathway activity status. The method includes: acquiring multi-omics data of a target object, wherein the multi-omics data includes multiple different omics entities; performing interaction strength analysis on each omics entity to obtain edge weight data between each omics entity; evaluating pathway activity on the edge weight data based on a pre-trained multilayer heterogeneous graph convolutional network to obtain pathway activity evaluation results of the edge weight data; and predicting the pathway activity status of the target object based on the activity evaluation results. This method can effectively capture the activity changes of genes in different omics entities, thereby improving the accuracy of biological pathway activity prediction.

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 invention relates to the technical field of data processing, in particular to a multi-omics data conjoint analysis and model training method and device, equipment and a medium, and the method comprises the steps: constructing a graph structure based on prior biological knowledge, selecting a center feature from vertexes of the graph structure, and taking other vertexes except the center feature in the graph structure as edge features; constructing a layer network layer based on the graph structure, wherein the first layer is an edge feature with the shortest path from each center feature as a hop in the graph structure; training a preset disease mechanism model by using the training sample to obtain a trained disease mechanism model; a biological pathway transmission layer in the disease mechanism model is used for transmitting the information of the molecular characteristics of the first layer in the molecular characteristics of the sample to the direct connection characteristics in the first layer, gradually transmitting the information of the edge molecular characteristics to the central characteristics of the zero layer, and obtaining the updated central characteristics for disease analysis. According to the technical scheme, path analysis details can be explained.

Disclosed herein are methods for treating complex disorders in human subjects and for preventing complex disorders in human subjects at risk of developing the disorder, including identifying one or more pharmacologically relevant biological pathways associated with a complex disorder and selecting an agent suitable for the treatment or prevention of the complex disorder. As described herein, the quantification of common variant enrichment in biological pathways with known drug targets provides a means of functionally annotating genome-wide polygenic risk scoring, which provides an indication of an individual's exposure to risk variants that are potentially treatable using existing pharmaceutical agents, dietary supplements or lifestyle interventions.

Orally-ingestible supplement formulations for boosting NAD+ (Nicotinamide adenine dinucleotide) in a human and methods of use. The formulations provide a Triple Boost for NAD+ that will combine 3 or more different ingredients each of which uses a different metabolic pathway selected from the following: a) the De Novo Pathway, b) the Preiss-Handler Pathway, c) the Salvage Pathway, d) the Reduced NAD+ Precursor Pathway, and e) CD38 Inhibition Pathway.

The invention discloses a deep learning and pathway analysis fused drug action mechanism analysis method and system, and the method comprises the steps: obtaining biological sample omics data after drug processing, and carrying out the cleaning and standardization processing; constructing a deep learning model based on a convolutional neural network and an attention mechanism, and training the model by using an omics data set marked with a known action target of a drug; inputting preprocessed omics data into the trained model, and screening key potential action targets; mapping the key potential action targets to a known biological pathway database, and screening significantly enriched pathways as potential drug action pathways; calculating a comprehensive score of each potential action pathway, and screening key action pathways; and outputting an analysis result in a visual form. According to the method, the target predictive ability of deep learning and the biological significance of pathway analysis are combined, the drug action mechanism can be accurately analyzed, and support is provided for drug research and development.

The invention relates to the technical field of biological recognition models, and discloses an Alzheimer's disease related biological pathway recognition method based on an intron retention level, a recognition model and a construction method of the Alzheimer's disease related biological pathway recognition model. The method comprises the following steps: firstly, collecting a plurality of data sets containing RNA-seq samples of human brains and blood; 2, calculating a gene expression quantity and an intron retention expression quantity of the sample; thirdly, constructing a standardized gene expression reading matrix and an introne retention reading matrix; 4, constructing an expression proportion matrix of the intron retained in the gene; 5, collecting biological pathway information in a plurality of human gene subsets; 6, constructing intron retention feature representation under the biological pathway by utilizing the biological pathway and intron retention expression proportion matrix; and finally, identifying the Alzheimer's disease related biological pathways by using the random forest model. According to the method, a new quantitative index can be provided for gene set analysis, and a biological pathway recognition feature matrix for AD is constructed based on the new quantitative index.