84 results about "Biological pathway" patented technology

The invention provides a feature fusion method and system based on multi-modal medical data, and belongs to the technical field of medical data processing. According to the method, the structural relationship, such as a patient network, a biological pathway and an anatomical structure, in the medical data is modeled by using # imgabs0 #, the extracted structured features are input into # imgabs1 # to capture global context information, and a self-adaptive mechanism can dynamically adjust the feature weights output by # imgabs2 # and # imgabs3 # according to the data modality, so that more accurate feature fusion is realized. Experimental results prove that the model provided by the invention is superior to other baseline models on a plurality of multi-modal medical data sets.

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 present disclosure provides methods for determining biological pathways and nucleotide polymorphisms (NPs) shared between two diseases.

An example method for quantitative systems pharmacology (QSP) includes analyzing, using a computing device, RNA sequencing (RNA-seq) data for a plurality of patients having a disease. The analysis identifies a plurality of differentially expressed genes (DEGs) and a plurality of differentially enriched biological pathways. Additionally, the method includes deriving, using the computing device, a plurality of gene expression signatures associated with each of a plurality of disease states of the disease using the DEGs and the differentially enriched biological pathways. The method also includes identifying, using the computing device, a plurality of drugs predicted to reverse a particular gene expression signature associated with a particular disease state of the disease. The method further includes prioritizing, using the computing device, the drugs predicted to normalize the particular gene expression signature associated with the particular disease state of the disease for further experimental testing.

As disclosed herein a precision based immunomolecular augmentation (PBIMA) high specificity patient profiling networked computer system, rapid therapeutic vaccine design method, and personalized vaccine, which utilizes immuno-molecular biopathway HLA affinity mapping and selection prediction ranking tools. This PBIMA approach comprises: Strategic-Selection, Molecular-Mapping, Antigen-Alignment, Receptor-Recognition, and Tactical Technology (SMART). The platform obtains data from a patient's genes and proteins as input. NGS data, including WES, WGS, ctDNA and cfDNA, RNAseq uses as input. PBIMA comprises a gene-protein-cell Cloud-based sequence editing interface to select the high confidence peptides. The PBIMA vaccine is a solution-based multi-purpose vaccine design strategy. PBIMA technology can produce therapeutic vaccines for cancer, autoimmune, neurodegenerative, inflammation-driven disease, and novel pathogen infection treatment. PBIMA therapeutic design is multi-mechanistic and broad-spectrum.

The invention discloses a method for mining a core hot spot gene based on a population transcriptome and application of the method in plant molecular breeding, and the method comprises the following steps: S1, processing and sampling a rice population under different nitrogen conditions to obtain a rice sample; and S2, carrying out differential expression gene analysis on the rice sample to obtain the group nitrogen response gene. And S3, carrying out correlation analysis on the expression level of the group nitrogen response gene as a molecular phenotype and genetic variation to obtain an expression quantitative character site of the group nitrogen response gene. And S4, screening out eGWAS hot spot regions according to expression quantitative character sites. And S5, performing eTWAS analysis on the group nitrogen response gene to obtain an eTWAS result. And step S6: screening out an eTWAS hot spot gene from an eTWAS result. And step S7, taking the intersection of the eTWAS hot spot gene and the gene in the eGWAS hot spot region to obtain a core hot spot gene. Through eGWAS-eTWAS conjoint analysis, core hotspot genes for regulating and controlling important biological pathways can be efficiently and accurately mined.

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 invention discloses a multi-omics data management system, and relates to the technical field of data management, an exception comparison module constructs a difference factor Cyyz by using a detection result and a second multi-omics data set, and judges an exception value based on the difference factor Cyyz, the process can identify and mark exception data as early as possible, the identification of the exception data is more accurate, and the efficiency is improved. And further analysis and research are facilitated. And the biological pathway identification module performs statistical analysis on the abnormal data, generates related state data information and analyzes a deviation coefficient Pcxs of the abnormal data in a corresponding biological pathway, and the analysis can reveal specific influence of the abnormal data in the biological pathway and help understand a biological mechanism of an abnormal state. The management module sets an evaluation threshold value and compares the deviation coefficient Pcxs with the evaluation threshold value so as to judge whether the samples in the experimental group are in a lesion state or not, the lesion state of the samples can be accurately evaluated in the process, and a scientific basis is provided for diagnosis and treatment of diseases.

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.

The present application relates to the field of image analysis technology, and in particular to a method and system for evaluating the prognosis of GBM patients based on magnetic resonance-pathology image fusion, the method comprising: obtaining a data set of GBM patients, and obtaining MRI images and pathology WSI, and obtaining transcriptome sequencing data; obtaining image-pathology fusion features based on MRI images and pathology WSI, and constructing an image-pathology omics model; obtaining image-pathology omics analysis results based on the image-pathology omics model and transcriptome sequencing data; using weighted gene co-expression network analysis and gene set enrichment analysis methods to analyze transcriptome sequencing data, obtain cross-biological pathways, and determine the key biological pathways behind the image-pathology omics features. The present application uses magnetic resonance and pathology images to obtain image-pathology fusion features and construct a fusion model to evaluate the incremental prognostic predictive value of image-pathology fusion omics labels for IDH wild-type GBM patients relative to traditional clinical pathological factors.

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 invention relates to the technical field of gene engineering, and discloses a molecular marking method and system for an RNA regulatory network of lncRNA UCA1 in cerebral ischemia injury, and the method comprises the following steps: establishing a cerebral ischemia injury scene of an experimental subject, and analyzing the interaction between the lncRNA UCA1 and other RNAs; the method comprises the following steps: performing scene grading on a cerebral ischemia injury scene, and measuring lncRNA UCA1, target RNA, a biological pathway of an experimental subject and lncRNA UCA1 expression related substances, target RNA expression related substances, biological pathway quantity and ischemia related cell data of ischemia related cells of the experimental subject in the graded scene; the cell numerical value of the ischemia related cell is calculated, and the lncRNA UCA1 expression level of the lncRNA UCA1 is analyzed by using the lncRNA UCA1 expression related substance; identifying cell-grading association and pathway-grading association between the cell numerical value and the grading scene and between the biological pathway quantity and the grading scene; the molecular marker in the cerebral ischemia injury scene is selected from the lncRNA UCA1 and the target RNA by using RNA-cell association and RNA-pathway association. According to the invention, the numeralization ability of the molecular marker in cerebral ischemia injury can be improved.

The invention discloses screening and identification of pseudomonas capable of producing high-yield feruloyl esterase and application of the pseudomonas as a biological enhancer in fermented food, and belongs to the technical field of brewing. According to the invention, a strain of pseudomonas sp. A-4H with high yield of feruloyl esterase is obtained by screening. After the pseudomonas A-4H is cultured for 36 hours, the content of ferulic acid which is 153.79 + / -2.36 mg / L can be generated, and the enzyme activity of feruloyl esterase in fermentation liquor reaches 136.63 + / -10.27 U / L; when the pseudomonas A-4H is added in the brewing process of the yellow rice wine, the ferulic acid content, phenolic acid content and organic acid content in the yellow rice wine can be effectively increased, and an effective biological way is provided for increasing the ferulic acid content in brewed food.

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.