27 results about "Gene interaction" patented technology

The invention discloses an accurate report automatic analysis and generation system based on gene detection data, and belongs to the technical field of bioinformatics and artificial intelligence. The system comprises a variation intelligent labeling module, a heterogeneous knowledge graph reasoning module, a scene adaptive report generation module and an intelligent quality control and feedback module; a three-layer heterogeneous knowledge graph containing gene-disease association, drug-gene interaction and clinical guide decision is constructed, variation-disease association reasoning is performed by adopting a relational graph attention network, scene adaptive generation of report content is realized in combination with a semantic slot filling mechanism, and the four modules form a closed-loop collaborative system through deep coupling. Gene detection data can be automatically converted into a clinical diagnosis report, and the report generation time is shortened from 48 hours to 30 minutes or less.

The invention discloses a mouse multi-gene collaborative insertion and knockout system based on multi-data coupling, and relates to the technical field of bioengineering, and the mouse multi-gene collaborative insertion and knockout system comprises a multi-gene collaborative targeting vector, a high-fidelity Cas9 protein expression unit, a homologous recombination template library, a multi-modal efficiency prediction module, a dual-fluorescence Cre report unit and an off-target inhibition element. According to the method, vector silencing is avoided by connecting the nuclear matrix attachment region in series with the gRNA expression cassette, and the multi-gene collaborative editing efficiency is improved to gt by combining homologous arm optimization design and a machine learning driven efficiency prediction model; 80%; cas9 fusion deaminase and a glycosylase inhibitor are combined with miRNA-mediated off-target inhibition, so that off-target sites of a whole genome are reduced to be less than or equal to 1 / sample; real-time monitoring of tissue specific expression is achieved by means of a double-fluorescence Cre reporting system, space-time regulation and control application is supported, and a high-reliability tool is provided for complex gene interaction research.

This invention provides a high-throughput method for constructing dual sgRNA libraries and its applications. The method utilizes high-throughput microarray synthesis technology to prepare a set of DNA fragments containing multiple dual sgRNA expression cassettes in a single step. After amplification, these fragments are assembled with a target vector containing a first promoter and a second gRNA backbone sequence in a first round of directed assembly to obtain a preliminary recombinant plasmid set. Then, linearized enzyme digestion and homologous recombination technology are used to insert a fragment containing a transcription termination sequence and a complete second promoter to complete the construction of the dual sgRNA expression unit. Finally, transformation and amplification yield the dual sgRNA plasmid library. This invention avoids the high error rate and high cost of long-chain oligonucleotide synthesis by utilizing microarray synthesis and simplifies the operation process through two rounds of directed assembly, significantly improving the throughput, fidelity, and efficiency of library construction. It is applicable to the construction of genome-wide dual sgRNA libraries, providing an efficient and reliable technical platform for high-throughput gene function screening, drug target discovery, and gene interaction research based on CRISPR.

This invention discloses a gene regulation inference method guided by topological data analysis and gene network embedding. It combines TDA and GNN to enhance the inference capability of gene regulation networks. By capturing the topological structure of the gene regulation network graph through TDA features, the model's ability to model gene expression is enhanced. The TDA features and GAT embedding representations are effectively integrated through gating fusion. This fusion mechanism enables the model to adaptively adjust node embeddings based on global topological characteristics, which not only improves the accuracy of gene interaction representation but may also enhance the accuracy of regulatory relationship prediction. The traditional GAT architecture is extended through a four-layer graph attention mechanism. Each layer uses residual connections to alleviate the gradient vanishing problem and improve training stability. In addition, independent multilayer perceptron branches are designed for transcription factors and target gene embeddings. This deep architecture can achieve more expressive feature transformations and capture subtle patterns in gene regulation networks.

The application provides a HBV inhibitor screening method based on a molecule-gene interaction constraint graph convolution network. In view of the limitation of a traditional drug discovery method in processing complex biological data, the application is based on a constructed compound library verified by anti-HBV in-vitro activity, a plurality of gene targets associated with the corresponding compound and an interaction network thereof, a graph data processing capacity of a graph convolution network model is used, and a molecule-gene interaction constraint graph convolution network model is constructed. The model combines an interaction matrix of a target protein corresponding to the gene, a gene feature matrix and a compound activity label, and effectively predicts the biological activity category of the compound. The specific steps include data processing, graph data generation, graph convolution network model training, hyperparameter optimization and model evaluation. The model parameter AUC value is 0.97, and the model effect is good. The application provides a new path and idea for virtual screening of anti-HBV drugs, and has potential application value.

The invention belongs to the field of plant molecular genetics and crop breeding, and provides a rapid identification method and a creation method of an extremely early rice variety. According to the invention, a specific genotype combination causing the rice to present an extremely early-maturing phenotype is clear and verified for the first time, i.e., the three genes Ghd7, Ghd7.1 and Ghd8 are represented as function deletion alleles, and meanwhile, the Hd1 gene is represented as a functional alleles. Based on the specific combination, genotype identification is performed on a plurality of known extremely early rice varieties in northeast and southern China, and results are completely identical. Furthermore, a novel rice material with the genotype combination is created through a gene editing technology, and a typical strain with an extremely early heading stage is successfully obtained, so that the accuracy and reliability of the combination are verified on the molecular level. According to the invention, the problem of inaccurate prediction caused by gene interaction complexity in the prior art is solved, and a powerful molecular tool is provided for early-maturing breeding of rice.

The invention discloses a whole genome gene-gene and gene-environment interaction detection method based on an interpretable genetic information neural network, which comprises the following steps of: 1, training a deep neural network model by using genotype data and phenotype data to obtain a trained deep neural network model; 2, inputting genome data of a sample, and predicting a phenotype value of the sample by using the trained deep neural network model; and step 3, calculating the xPI value of each genome variation site, and carrying out whole genome association analysis and genetic site detection through the xPI values. By constructing the deep neural network model and combining the genotype data and phenotype data of the sample, the target character is accurately predicted. Through trained network analysis, the xAI-GWAS not only quantifies the importance of each genotype data, but also can detect the additive effect, the gene interaction effect and the gene-environment interaction effect of the genotype data.

This disclosure describes a machine learning (ML) framework, including a graph convolutional neural network (GCN), for identifying gene expression signatures associated with cancer driver events. The model is trained to identify the TP53 mutation status of cancer samples from gene expression, utilizing a comprehensive, curated graph structure of gene interactions. Quantitative scores are generated to rank the severity of driver events in each sample. Very high AUC results for unknown data across several tumor types are achieved in this method. A strong correlation with protein function exists. The Signature in Transcriptome Associated with Mutant Proteins (STAMP) model can also predict driver events in many combinations of key oncogenes / pathways and several tumor types, based on well-established annotations from the literature. Thus, the STAMP model can identify and quantify driver events, which may lead to improved targeted therapy selection and prioritization in cancer patients.

A computerized system and method are provided for assessing a number of gene-gene interactions between the HLA and IRF5 gene regions. At least one computing device enrolls subjects in a registry, including SLE patients having met classification criteria for SEE and Sjogren's patients having met AECG criteria. Moreover, at least one computing device can perform genotyping for the subjects and healthy control subjects, for submission to a genotyping platform. Further, at least one computing device can develop HLA risk factor models for each of a plurality of stages, and perform statistical analysis for each of the plurality of stages.

The application discloses an immune pre-pneumonia evaluation method and system based on a machine learning algorithm, and relates to the technical field of immune evaluation.The application constructs a machine learning algorithm model based on multiple specific HLA subtypes, captures the correlation between gene interaction and immune pathways through a time sequence network, combines multi-task loss and adversarial training to enhance robustness, and realizes high-precision pre-risk evaluation of immune pneumonia caused by immunotherapy.The scheme solves the problems of the prior art, such as dependence on lagging symptoms, insufficient generalization ability of prediction markers, and sensitivity to data noise, has good biological interpretability and clinical practicability, can be extended to other immune side reaction prediction, and provides reliable support for personalized treatment decisions.

The application provides a biotin-labeled exogenous circular DNA and a construction method and application in protein interaction thereof, and belongs to the technical field of gene interaction. The application provides a construction method of the biotin-labeled exogenous circular DNA, wherein a target sequence is amplified by using a biotin-labeled primer to obtain linear DNA containing a preset restriction enzyme cutting site; the obtained linear DNA is subjected to single enzyme cutting treatment by using a corresponding restriction endonuclease; and the DNA after enzyme cutting is subjected to a self-ligation reaction to generate closed circular DNA. The preparation process of the biotin-labeled circular DNA is stable and controllable, and has high repeatability; the biotin labeling of the circular DNA can be combined with a streptavidin system to realize high-affinity and specific enrichment of a DNA-protein complex; and the circular DNA can be stably delivered in cells and maintain the structural integrity of the circular DNA, thereby providing an effective means for truly reflecting the interaction between the circular DNA and the protein in a physiological environment.

The present invention discloses a gene identification method based on a masked graph autoencoder, which comprises the following steps: 1. obtaining gene interaction data and omics feature data and performing preprocessing; 2. masking the processed data, specifically including two branches, a node masking module and an edge masking module; 3. inputting the masked network into a graph autoencoder for training, which learns the network embedding representation by reconstructing the nodes and edges of the network; 4. obtaining a low-dimensional embedding of features through the trained encoder, and finally using a logistic regression classifier to classify genes. The present invention simultaneously focuses on the node information and structural information of the graph by masking the nodes and edges in the network respectively, and reduces the dependence of the feature training model on label information in a self-supervised learning manner, thereby accurately classifying genes.

The invention discloses a Kolmogov-Arnod network-based whole genome prediction method, which comprises the following steps of: obtaining a reference genome and genotype data to be predicted, annotating single nucleotide polymorphism (SNP) in the genotype data to be predicted based on the reference genome to obtain a genotype matrix of a gene level, the method comprises the following steps: acquiring a gene level genotype matrix, performing dimension reduction processing on the gene level genotype matrix by using a gene-principal component analysis (Gene-PCA) method to obtain low-dimensional representation of high-dimensional genotype data, inputting the obtained gene level genotype matrix into a pre-trained gene expression level prediction model to obtain a predicted gene expression profile, and predicting the gene expression level. And inputting the obtained low-dimensional representation of the high-dimensional genotype data and the predicted gene expression profile into a pre-trained whole genome prediction model to obtain a predicted phenotype value corresponding to the genotype data to be predicted. According to the method, the technical problem that the prediction precision is insufficient due to the fact that an existing linear statistical method cannot describe gene interaction and nonlinearity can be solved.

The invention discloses a targeted senile degenerative bone disease key lesion regulation factor mRNA therapy recommendation evaluation model method, and the model comprises a data input layer which receives bone disease genetic association and regulation data, including GWAS summary data, space transcriptome and single cell transcriptome data; the intervention target priority ordering module is used for integrating the obtained genetic evidence, regulation evidence and network evidence and carrying out priority ordering on intervention targets; and the intervention target identification network module is used for predicting a key intervention target according to an intervention target network generated by acquiring a gene interaction relationship from the pathway, and obtaining a potential mRNA intervention therapy. The genetic evidence comprises a genetic risk site set annotated through multi-modal regulation genomics data; the regulation evidence comprises functional genomics data related to diseases; the network evidence comprises a high-credibility protein interaction relationship. And the intervention target identification network module is used for analyzing an intervention target network and further comprises disturbance removal analysis and regulation and control hierarchy analysis.

A method for determining gene to gene interaction based upon gene modification is disclosed. The method includes: (i) extracting a cooperative network implementing an unsupervised regression tree; (ii) classifying a plurality of gene interactions into one of up-regulation category and / or down-regulation category; (iii) creating a gene expression profile matrix based upon gene data and resulting expression weighting functions; (iv) receiving a request to modify a gene and / or an expression of the gene; and (v) simulating an interaction of the modification within the gene expression profile matrix to provide a weighted direction graph.

The present disclosure describes a machine learning (ML) framework including a graph convolutional neural network (GCN) for identifying gene expression features associated with cancer driven events. The model is trained to identify the TP53 mutation status of a cancer sample from gene expression using a comprehensive selected gene interaction map structure. A quantitative score is generated to rank the severity of the drive events in each sample. By means of the method, an extremely high AUC result is achieved on unseen data of multiple tumor types. And the gene has strong correlation with protein functions. Based on the annotations established in the literature, a transcriptome feature (STAMP) model associated with the mutant protein can also predict driving events of a variety of important cancer gene / pathway combinations and a variety of tumor types. Therefore, the STAMP model can identify and quantify driving events, which can provide a new way for cancer patients to improve selection and priority ranking of targeted therapy.

The invention discloses a method for constructing a chromosome three-dimensional structure based on fusion gene data and application of the method, and belongs to the technical field of genomes. According to the method, internal interaction of fusion genes and interaction between chromosomes are analyzed, hierarchical structures of second, third and fourth stages of the chromosomes are determined by calculating the frequency of the interaction, and a three-dimensional structure is constructed according to the hierarchical structures of the second, third and fourth stages of the chromosomes; the method is of great significance in exploring gene interaction and determining the relation between the chromosome spatial position and the disease.

According to the present invention, a system: acquires a dataset composed of gene expression data of a sample belonging to either of two groups; generates a gene network representing interactions between a plurality of genes by referring to a pathway database; selects one gene from among the plurality of genes; extracts a path by following the gene network downstream with the selected gene as a starting point; calculates path scores by executing enrichment analysis by using, as inputs, a list of genes included in the path and the dataset; generates a ranked gene list by ranking the plurality of genes on the basis of the path scores of the plurality of genes; and calculates drug scores by executing the enrichment analysis by using, as inputs, a list of target genes of drugs stored in a drug database and the ranked gene list.

The invention provides a wheat germ processing scheme recommendation method and system combined with deep learning. The wheat germ processing scheme recommendation method comprises the following steps: firstly, acquiring multi-dimensional basic data (including variety characteristics, raw material quality and microstructure image data) of wheat germs and processing scene demand parameters (including processing product application scenes, processing capacity adaptation and processing resource constraint data); performing feature gene extraction processing on the data to obtain a wheat germ feature gene set and a scene demand feature gene set; inputting the sequence into a pre-trained depth scheme evolution model, and generating a processing link feature gene sequence through a gene interaction layer; constructing a plurality of processing scheme prototypes based on the processing link characteristic gene sequence; and finally, performing double-circulation adaptive optimization on the processing scheme prototype and the feature gene set to obtain a target processing recommendation scheme, and transmitting the target processing recommendation scheme to a processing execution terminal, thereby realizing scientific and accurate wheat germ processing scheme recommendation.

Target genes are identified with high accuracy and the effectiveness of drugs is evaluated. [Solution] The system acquires a dataset consisting of gene expression data of samples belonging to one of two groups, references a pathway database to generate a gene network representing the interactions of multiple genes, selects one gene from the multiple genes, traces the gene network downstream starting from the selected gene to extract a path, calculates a path score by performing enrichment analysis using as input a list of genes included in the path and the dataset, ranks the multiple genes based on the path scores of the multiple genes to generate a ranked gene list, and calculates a drug score by performing enrichment analysis using as input a list of genes that are targets of drugs stored in a drug database and the ranked gene list.

The application discloses application of F-box gene OsFBX49 in rice resistance to brown planthopper and belongs to the technical field of plant genetic engineering. The amino acid sequence of the protein coded by the gene OsFBX49 is shown as SEQ ID NO. 1. In the application, the OsFBX49 gene is transferred into rice susceptible to planthopper through agrobacterium-mediated genetic transformation, and the result shows that the plant overexpressing the OsFBX49 gene has enhanced resistance to brown planthopper. After the brown planthopper feeds on the transgenic plant, the survival rate of the brown planthopper is obviously reduced, the weight gain of the brown planthopper is obviously reduced, and the amount of honeydew secreted by the brown planthopper is obviously reduced. The gene in the application provides a basis for researching the gene interaction between rice and brown planthopper and has a reference significance for researching the molecular function of genes and breeding.

The invention discloses a Cas12i and excision enzyme fusion mediated fixed-point insertion method for a plant. The invention provides a vector for fixed-point insertion based on CRISPR gene editing, the vector expresses a fusion protein containing exonuclease and Cas12i and can generate a repair template at the same time, and the repair template has three forms, namely, the repair template exists in a DNA form, the repair template exists in a DNA + RNA form, and the repair template exists in an RNA form. By comparing the influences of different template strategies, fusion excision enzymes and the like on the endogenous gene site-specific insertion efficiency of cells, especially plant cells, a Cas12i and excision enzyme fusion mediation-based plant site-specific insertion technology system is established, and a foundation is laid for research on crop important gene function analysis, intracellular gene interaction and molecular network analysis. And important technical support is provided.

Disclosed herein are methods for identifying a pair of genes comprising a synthetic lethality (SL), synthetic rescue (SR), or synthetic dosage lethality (SDL) interaction. The method is composed of two independent models. Also, disclosed herein are uses thereof.

The application provides a wheat germ processing scheme recommendation method and system combined with deep learning, first, multi-dimensional basic data of wheat germ (including variety characteristics, raw material quality, microscopic structure image data) and processing scene demand parameters (including processing product application scene, processing capacity adaptation, processing resource constraint data) are acquired; then, feature gene extraction processing is performed on the above data to obtain a wheat germ feature gene set and a scene demand feature gene set; the feature gene set and the scene demand feature gene set are input into a pre-trained deep scheme evolution model to generate a processing link feature gene sequence through a gene interaction layer; a plurality of processing scheme prototypes are constructed based on the processing link feature gene sequence; finally, double-cycle adaptive optimization is performed on the processing scheme prototype and the feature gene set to obtain a target processing recommendation scheme and transmit the target processing recommendation scheme to a processing execution terminal, so that scientific and accurate wheat germ processing scheme recommendation is realized.

The application discloses a chemical-gene interaction prediction method and system based on deep learning, and the method comprises the following steps: obtaining a chemical-gene interaction dataset of multiple cell lines; constructing a multi-dimensional chemical feature vector and a gene feature vector according to the chemical-gene interaction dataset of the multiple cell lines; constructing a bias perception neural network model according to the multi-dimensional chemical feature vector and the gene feature vector, the model decouples the interaction effect, the gene bias effect and the chemical bias effect through a bias perception interaction layer, and obtains an output representation; constructing an independent bias perception model for each cell line according to the output representation, and training and evaluating by adopting a multi-stage data splitting strategy; inputting a chemical-gene pair to be predicted into the trained bias perception model, and outputting a gene expression influence prediction result. The application can effectively process the cell heterogeneity problem and provide a biologically interpretable prediction result.

The invention discloses establishment and application of a gill cover bone tissue osteoblast line of humpback perch, and relates to the technical field of biology. The cell line is named as a gill cover bone tissue osteoblast line CAOBOB of the humpback perch, and is preserved in the China Center for Type Culture Collection (CCTCC) on May 17, 2025, and the preservation number is CCTCC NO: C2025130. The cell line can be applied to establishment of cell models related to skeletal development and gill bone malformation of the humpback perch. The invention relates to an application in a cell model for researching an osteoblast differentiation mechanism, cell morphology and dysfunction and a gill cover bone development regulation mechanism of humpback perch. The invention is applied to research on the causes of skeletal development and gill bone deformity of the humpback perch and screening of related reagents for improving skeletal development deformity. The invention is applied to cell models for humpback perch osteogenesis gene function verification, promoter activity analysis and gene interaction research. The cell model is applied to species traceability verification of a humpback perch source cell line and cell genetics research.

The application provides a gene coding breeding prediction method and device based on graph clustering, constructs a gene graph based on the strength of the correlation between genes, solves the gene graph by clustering to obtain the number of common regulatory gene groups and the gene group cluster number information of each gene, fuses the gene allele information and the gene group cluster number information to obtain the gene cluster coding of the sample, constructs a deep convolutional neural network based on the gene cluster coding information and the biological phenotype information to be predicted to optimize the gene breeding prediction performance. The application fully utilizes the gene interaction relationship network contained in the gene graph, can effectively extract the regulatory gene features for controlling the biological phenotype output, solves the problem of insufficient gene interaction relationship coding between the input coding layers of the classical model and the gene graph, guarantees the accuracy of the gene breeding prediction of the biological phenotype, and further improves the speed, efficiency and quality of the gene breeding, especially the yield.