30 results about "Gene association" patented technology

The invention provides a topology and biological feature fusion-based virulence gene prediction method, which comprises the following steps of: obtaining a to-be-detected gene; inputting the to-be-detected gene into a trained DAVGAE model, and predicting the correlation degree of the to-be-detected gene and the disease to obtain a disease gene correlation prediction conclusion; wherein the DAVGAE model comprises a data enhancement module, an encoder and an inner product decoder. The problems of data sparsity and heterogeneous data integration in gene-disease association prediction can be effectively solved at least through a DAVGAE model formed by a data enhancement module, an encoder and an inner product decoder.

This invention provides compositions and treatment methods for reducing hyperphenylalaninemia. [Solution] A genetically modified bacterium is provided, comprising: a) one or more genes encoding phenylalanine ammonia lyase (PAL), which are operably linked to a promoter that is not naturally associated with the PAL gene and can be directly or indirectly induced; b) one or more genes encoding a phenylalanine transporter, which are not naturally associated with the phenylalanine transporter gene and can be operably linked to a promoter that is directly or indirectly induceable; and c) one or more genes encoding mutant fumarate and nitrate reductase (FNR), which are not naturally associated with the FNR gene and can be operably linked to a promoter that is directly or indirectly induceable.

The application discloses a gene data processing method and device, computer equipment and a storage medium, and belongs to the technical field of computers. Through a gene function query request of a to-be-tested gene, the application can call a gene association model corresponding to a cell type to which the to-be-tested gene belongs, mine a nonlinear association degree between the to-be-tested gene and a known candidate gene, and use the candidate gene with a higher nonlinear association degree to label function annotation information of the to-be-tested gene. The way of calling the gene association model to extract the nonlinear relationship is completely different from the way of extracting the linear relationship in traditional statistics, can deeply mine the candidate gene with a higher similarity to the to-be-tested gene, and the similarity is not a linear similarity but an implicit nonlinear similarity, so that the accuracy of the gene data processing process is greatly improved.

The application provides a multi-party medical privacy data security sharing method based on privacy protection and TEE. The method comprises the following steps: obtaining local calculation results of multiple participants; the distribution frequency of alleles comprises a first frequency of a first gene appearing at a first site on a SNP, a second frequency of a second gene appearing at a second site, a third frequency of a first genome appearing, and a fourth frequency of a second genome appearing; the local calculation results of each participant are combined to obtain a to-be-analyzed coordinate site of each participant; the to-be-analyzed coordinate site is clustered to obtain a first center point of a first coordinate site and a second center point of a second coordinate site, and the real frequencies of the first gene, the second gene, and the first gene and the second gene appearing at the same time are determined; the real frequencies are used for correlation degree calculation to determine the gene correlation between the genes at the first site and the second site on the SNP. The application determines the gene correlation between the SNP sites, which is convenient for the research on genes.

The present invention discloses a method, system and storage medium for predicting the association relationship between Chinese herbal medicines and genes, which relates to the field of bioinformatics technology. Convert the components of Chinese herbal medicines into molecular graphs to generate initial molecular structure features; optimize the initial molecular structure features through a substructure-aware network to obtain target molecular structure features; divide the gene sequence into several subsequences and construct a k-mers graph to obtain gene subsequence features; through multi-level feature fusion, aggregate the target molecular structure features and the gene subsequence features; calculate the joint probability distribution between the component embedding representation and the gene embedding representation to obtain the predicted relationship. The method for predicting the association relationship between Chinese herbal medicines and genes based on core substructure perception in the present invention not only extracts gene sequence features, but also extracts the structural features of Chinese herbal medicine components, fully considering the structural information of Chinese herbal medicine components and genes, and the prediction result of the association relationship is more accurate and has high universality.

The invention relates to a disease similarity prediction method and system based on multivariate data fusion, and the method comprises the steps: collecting gene network data, miRNA network data, a disease-gene incidence matrix and a disease-miRNA incidence matrix, and forming a unified heterogeneous biological network; extracting and generating a multi-modal feature set through the graph convolutional network and the improved graph attention network; the independent weight of each feature in the multi-modal feature set is calculated through a multi-layer perceptron, gene view angle embedding and miRNA view angle embedding of the disease are generated in combination with the incidence matrix and serve as model input training, and a lightweight bilinear tower model is obtained; model parameters are optimized through an alternate training strategy and a regularization mechanism inspired by ReconBoost, an optimized lightweight bilinear tower model is obtained, and a similarity prediction result of the target disease pair is obtained. The defects of multivariate data integration, sparse data processing and prediction stability in the prior art are effectively overcome.

The invention belongs to the technical field of genes, discloses a method for detecting and mining associated genes and regulatory pathways by using double-layer optimization, solves the black box problem in a machine learning prediction process and provides biological mechanism explanation. The previous research mostly utilizes multi-omics data to predict phenotypes, but a machine learning mode is difficult to really evaluate the action size and action approach of genes, and lacks interpretability at the same time. According to the RADAR, a parameter is given to each omics of each gene for double-layer optimization solution, phenotypes can be explained to the maximum extent, and meanwhile the action degree of each omics can be evaluated. In addition, RADAR can process input data of any number of omics, and a program can automatically identify the number of the omics and calculate the effect size of each omics. RADAR can accept omics input of different gene numbers, and the actual data condition is better met.

The invention discloses a scoring and sorting method and system for pathogenic mutation of a single-gene genetic disease, and relates to the technical field of biomedical treatment, the method comprises the following steps: based on pre-acquired phenotypic data and literature abstracts, performing standardized phenotypic term extraction on pre-acquired symptom description by using a mixed strategy, performing phenotype and gene association degree scoring on the standardized phenotype terms and pre-acquired gene mutation data through a similarity comparison method; performing gene variation annotation on a pre-acquired gene variation database, and performing gene variation scoring on the pre-acquired gene variation data according to a gene variation annotation result; and performing multi-dimensional pathogenicity comprehensive scoring evaluation by using a supervised learning model and a weighted summation method to obtain a pathogenic mutation sorting scoring result. By recommending the most probable pathogenic mutation, the grading and sorting of the pathogenic mutation of the genetic disease have the advantages of high detection recall rate and high automation degree.

The invention discloses a multi-gene interaction network prediction method and system based on a graph neural network. The method comprises the following steps: S1, acquiring multi-source biological data; s2, carrying out pretreatment; s3, constructing a gene map; s4, fusing the edge weight matrix and the node feature matrix in the gene map through a dynamic attention mechanism, and outputting a refined feature set; s5, acquiring an optimal hyper-parameter set by using a Bayesian optimization algorithm; s6, based on the gene map and the optimal hyper-parameter set, training the attention enhancement map neural network to obtain a multi-gene interaction network prediction model; and S7, predicting the interaction relationship of the unknown gene pair by using the multi-gene interaction network prediction model, and outputting a result. According to the method, the gene map is constructed through multi-source data fusion, the gene association information is enriched, the multi-head attention map neural network is introduced to automatically learn the interaction weight, the hyper-parameters are dynamically adjusted in combination with Bayesian optimization, and the prediction accuracy and model universality of the multi-gene interaction network are improved.

The invention discloses a rubber tree whole genome SNP molecular marker combination and gene chip and application, belonging to the field of molecular marker development. The rubber tree whole genome SNP molecular marker combination includes 28923 SNP molecular markers, and the 28923 SNP molecular markers include 28923 SNP sites, each SNP site includes two different base variation sites, for detecting the allele change of the site, and the physical location of the 28923 SNP sites is determined based on the genome sequence comparison of the rubber tree wild germplasm MT / VB / 25A 57 / 8; the 28923 SNP sites adopted by the present invention are evenly covered on the rubber tree whole genome, can fully reveal the true level of rubber tree genetic diversity, can be applied to the genetic diversity analysis, population structure analysis, whole gene association analysis, molecular marker assisted breeding and whole genome selection breeding research work of rubber tree. Utilizing the whole genome SNP site combination of the present invention and its gene chip to carry out detection, there is the advantage of being simple, fast and low in cost, and can realize large-scale application in rubber tree genetic breeding research.

The invention discloses a gene prediction system based on phenotype-gene relevance and a storage medium, and the system comprises a data integration module, a knowledge graph construction module, a neural network module and a rapid updating module. The data integration module is used for integrating a plurality of medical databases to obtain a multi-modal database; the data of the multi-modal database is in a gene-disease-phenotype association form; the knowledge graph construction module is used for constructing a knowledge graph according to the data of the multi-modal database; the neural network module is used for processing the preprocessed data according to a knowledge graph and a multi-modal database to obtain a pathogenic gene prediction result; and the rapid updating module is used for updating the multi-modal database, the knowledge graph and the neural network module. According to the application, a plurality of medical databases are integrated through the data integration module to obtain the multi-modal database, and the preprocessed data are processed through the neural network module to obtain the pathogenic gene prediction result. Through correlation of phenotypes and genes, the accuracy of gene prediction is improved.

The present invention provides a crop whole-genome prediction method and system based on machine learning. First, a gene expression data set within multiple growth cycles of a target crop is obtained. The gene expression data set contains multiple gene sequence samples composed of genetic markers and phenotypic trait data. Next, feature extraction is performed on the gene expression data set to obtain gene association features and growth trait features. Then, a preset machine learning model is used to perform a fusion prediction of the gene association features and growth trait features to generate a fusion prediction feature. A whole-genome prediction result is determined based on the fusion prediction feature. The whole-genome prediction result can indicate the trait expression trend of the crop under different environments. Finally, an adaptive optimization strategy is generated based on the whole-genome prediction result and fed back to the crop cultivation system to trigger the adjustment of cultivation parameters, thereby realizing precise and intelligent crop cultivation and improving cultivation efficiency and crop adaptability.

The invention relates to the field of bioinformatics, in particular to a susceptibility gene visualization decision support system and a method thereof.According to the system, a multi-level heterogeneous network of family and gene variation is constructed, topological feature extraction and Laplacian spectrum analysis are combined, a high-risk variation transmission path is recognized, and a high-risk variable transmission path is obtained; multi-scale persistent homology analysis is utilized to reveal risk structure hierarchies, dynamic display of a family-gene association map is realized through interactive visualization, finally, a risk hierarchy tree and a clinical knowledge base are integrated, accurate clinical decision support is provided for users, a complete analysis process from data to decision is realized, and the risk analysis efficiency is improved. A multi-level heterogeneous network is constructed based on a topology invariant theory, and integration expression of family relationship and gene variation data in a unified topology space is realized for the first time, so that doctors can intuitively understand distribution and association modes of variation in families.

This invention provides a method and application for identifying molecular markers associated with tail fat weight in sheep based on a multi-omics strategy. The method is based on a comprehensive analysis of whole-genome selection signal analysis, whole-genome association analysis of tail fat weight, and epigenomic and transcriptomic sequencing results of tail adipose tissue in sheep with different tail types. The marker is located at position 51760995 on chromosome 13 of the sheep reference genome Oar_rambouillet_v1.0, where an A / C base mutation exists, which is significantly correlated with tail fat weight. Amplification of the genomic region containing the SNP site statistically verified that the tail fat weight and relative tail fat weight of individuals with the AA genotype are significantly lower than those with the CC genotype. By detecting the genotype of the molecular marker, this invention allows for the selection of homozygous AA sheep for breeding in the core population, thereby reducing tail fat deposition and improving the economic benefits of sheep farming.

This invention belongs to the interdisciplinary field of bioinformatics and traditional Chinese medicine (TCM), and discloses an algorithm for the improvement and prediction of TCM compound prescriptions, solving the problems of strong subjectivity, long cycle, and low efficiency in traditional TCM compound prescription improvement. The algorithm first acquires and preprocesses omics sequencing data from disease and healthy groups, then establishes TCM-target gene associations based on the HIT2 and TCMSP databases, and constructs a three-dimensional association matrix of TCM-target genes-5hmC loci for epigenomic data. After dimensionality reduction and extraction of core principal components using PCA, the sample-TCM association score is calculated and a matrix is ​​constructed. The p-value is obtained through t-test or Wilcoxon rank-sum test, and converted into DAS values ​​to quantify the regulatory activity of TCM, thus screening TCM for compound prescription improvement. This invention's algorithm is objective, highly targeted, efficient, and widely applicable, adaptable to various omics data and disease scenarios. Its effectiveness has been verified in a coronary atherosclerosis case, providing precise technical support for TCM compound prescription screening and optimization, personalized TCM treatment, and the development of innovative TCM drugs.

The invention belongs to the technical field of wheat molecular breeding, and particularly relates to an SNP site closely linked with a wheat plant high-tightness major gene and a corresponding KASP marker. The major gene is a QTL (Quantitative Trait Loci) gene qPH5A.1 located on a 5AS chromosome. In the application, the inventor performs detailed measurement statistics and analysis on the wheat plant height character of a group material on the basis of a DH system material of Zheng 1088 / CD87 and an F2 group material of CD87 obtained in early-stage work. The Whaas547101 molecular marker closely linked with the wheat plant height related major gene is obtained through preliminary mining by combining related gene sequencing and gene chip analysis results. Based on the results, excellent allele variation types can be identified in wheat breeding early generation materials, so that a scientific basis can be provided for wheat breeding progeny material selection, and the breeding efficiency of high-quality wheat can be improved.

The application relates to the technical field of canine genotype detection, and discloses a canine genetic disease genotype detection algorithm system and electronic equipment, which comprises a gene sequence feature extraction module, a site polymorphism identification module, a variation site screening module, a genotype grouping operation module, a pathogenic gene correlation module and a detection result output module. Each module is sequentially connected to realize efficient data transmission and processing. The variation site screening module, the genotype grouping operation module and the pathogenic gene correlation module each comprise four functional units, which are used for completing site screening, genotype grouping and pathogenic gene correlation analysis through multi-step fine processing. The system is provided with a built-in special analysis model, can integrate multidimensional data features to carry out detection operation, realizes whole-process processing from raw data to a standardized detection report, significantly improves the accuracy and comprehensiveness of canine genetic disease genotype detection, and provides reliable technical support for canine genetic disease diagnosis, breeding optimization and prevention and control.

The invention provides a drug gene association prediction method and system based on multi-view comparative learning, and relates to the crossing field of biological medicine and artificial intelligence technology. The method comprises the following steps: constructing a drug-gene interaction view and a semantic view; performing neighborhood information aggregation on each view, and updating vector representation of nodes in each view after arrangement; fusing the vector representations of the nodes in each view through inner product operation, and adding to obtain a prediction score; the vector representation of each view node is subjected to comparative learning, so that the model discrimination capability is improved; calculating a BPR loss function, and optimizing model parameters; and forming a multi-task joint optimization strategy training model taking a prediction task as a main task and a contrast learning task as an auxiliary task, and completing the training if the loss of each task tends to be stable. According to the method, through multi-view comparative learning, the potential relation between known drugs and genes is deeply explored, so that the accuracy of drug-gene correlation prediction is improved.

A method is described for determining a survival prognosis of a patient suffering from a breast tumor, using processing carried out by electronic processing and / or calculation means. The method first comprises step (a) of defining a biological network representative of a particular biological process associated with the breast tumor. The biological network comprises a plurality of nodes, a set of directional relationships between these nodes and a set of genes associated with these nodes. The method also includes step (b) of accessing a data set related to the patient, comprising gene expressions in a biological sample of the tumor isolated from the patient; and step (c) of calculating a continuous expression value for the aforesaid nodes of the biological network. If the node is associated with only one gene and it is found that the gene is present in the biological sample, the continuous expression value of the node is calculated as the expression of the associated gene detected in the biological sample. If the node is associated with multiple genes, and it is found that at least one of the aforesaid genes is present in the biological sample, the continuous expression value of the node is calculated based on the expressions of the associated genes, present in the biological sample. If the node is not associated with any gene, or the associated gene is not found in the biological sample, the node is marked as a node not associated with a continuous expression value. The method then comprises the following steps, carried out by the electronic processing and / or calculation means: (d) binarizing the data set of continuous expression values calculated for each node of the biological network to which a continuous expression value is associated, based on a comparison of the continuous expression value with a respective threshold, to thus obtain a first binarized data set of the nodes, obtained based on the detections made; (e) calculating an aggressiveness score based on the aforesaid first binarized data set of the nodes; and finally (f) determining a survival prognosis result based on the aforesaid aggressiveness score calculated.

The invention discloses a peanut drought-resistant gene associated molecular marker screening optimization system and method, and relates to the technical field of agricultural biology. The system comprises a data acquisition module, a molecular marker analysis module and a data integration and evaluation subsystem, peanut DNA, physiological indexes and environmental data can be acquired, candidate markers are identified through PCR amplification and high-throughput sequencing, and a comprehensive evaluation model is constructed to calculate correlation coefficients. The method comprises the steps of sample preparation and pretreatment, molecular marker preliminary screening, marker verification and optimization and application verification, setting of a drought and normal moisture control environment, elimination of false positive markers through repeated verification, screening of high-correlation markers with a correlation coefficient greater than or equal to 0.6, and application accuracy greater than or equal to 85%. The method solves the problems of low accuracy, poor environmental suitability and the like in the prior art, improves the marker screening accuracy and stability, can efficiently assist in cultivation of new drought-resistant peanut varieties and relieve the restriction of drought on the peanut industry, and has important scientific research and application values.

The invention relates to the field of disease detection, in particular to a newborn disease dynamic monitoring system based on phenotype and gene correlation analysis, which comprises a phenotype data acquisition module, a gene data acquisition module, a cell metabolism data acquisition module, a correlation analysis module, an early warning module and a man-machine interaction module, the phenotype data acquisition module is used for acquiring various phenotype data of the newborn; the gene data acquisition module is used for acquiring gene data of the newborn; the cell metabolism data acquisition module is used for acquiring various cell metabolism data of the newborn; the correlation analysis module is used for outputting disease risk indexes; the early warning module is used for monitoring disease risk indexes in real time; and the man-machine interaction module is used for displaying various acquired data and disease risk indexes of various diseases. According to the method, the stability of cell metabolism is considered in the calculation of the disease risk index, so that the disease risk can be reflected more accurately and comprehensively through the disease risk index.

The invention discloses a rice grain folic acid content related SNP molecular marker and application thereof. According to the invention, the content of folic acid in rice germplasm resource population grains is determined, and candidate gene association analysis is combined to identify that two SNP loci significantly associated with the content of 5-methyltetrahydrofolic acid in rice grains exist in the rice folic acid synthesis bifunctional enzyme hydroxymethyl dihydropterin pyrophosphatase / dihydropterioic acid synthase gene; sNP1 is located in a gene promoter region-1675bp, the polymorphism is G / C / T, SNP2 is located in a gene promoter region-1624bp, the polymorphism is C / T, the folic acid content of rice grains carrying SNP1 (G / C) and SNP2 (C) is remarkably higher than that of SNP1 (T) and SNP2 (T) type rice materials, the SNP1 (G / C) and SNP2 (C) are excellent genotypes for folic acid accumulation, and the two SNP molecular markers can be used for breeding folic acid-rich rice varieties.

The application discloses a method for screening complex disease drugs based on whole genome association signals, and comprises the following steps: screening and processing complex disease cells through a DESE algorithm based on whole genome association signals, so as to obtain a cell line of the complex disease; inducing and analyzing the cell line of the complex disease through drug blank control, so as to obtain a specific perturbation spectrum of the drug on the gene; and performing cyclic prediction analysis and calculation on the specific perturbation spectrum of the drug on the gene in combination with conditional data, so as to obtain a complex disease susceptible gene drug. Through the application, the screening range of complex disease candidate drugs can be reduced by predicting the drug for specifically perturbing multiple disease susceptible genes. The application can be widely applied to the technical field of high-throughput drug screening.

The invention provides an intelligent disease identification and classification method and system based on multi-modal data fusion, and the method comprises the steps: S1, collecting multi-modal data of a medical image, a physiological signal and genome data, and carrying out the preprocessing of the multi-modal data; s2, extracting multi-scale features from the medical image through a convolutional neural network, extracting time sequence features from the physiological signal by using a bidirectional LSTM network, and extracting gene association features from the genome data based on a graph neural network; s3, fusing the three modal features by using a self-attention mechanism, dynamically adjusting the weight, and generating a global feature; and S4, inputting the global features into a classification network, and completing disease classification based on a cross entropy loss function. The system comprises a data preprocessing module, a multi-modal feature extraction module, a fusion module and a classification module, multi-modal deep fusion, self-attention weight distribution and modular design are provided, and the accuracy, robustness and clinical adaptability of disease classification can be improved.

The invention discloses a cytodynamic modeling and gene regulation network construction method and system based on RNA rate, and the method comprises the steps: obtaining single cell RNA sequencing data, and constructing a gene expression matrix according to the unspliced abundance and spliced abundance of a gene; constructing a transcription kinetic model based on an ordinary differential equation, and modeling transcription kinetic parameters into functions about an unspliced abundance vector, a spliced abundance vector, a cell embedding vector and a prior gene regulation and control network; building a deep learning model architecture, constructing a loss function through a cellular level and a gene level, and guiding the deep learning model to fit the function to obtain transcription kinetic parameters; and extracting an attention matrix from the deep learning model obtained by training as an inferred single-cell gene regulation and control network. According to the method, the problems of inaccuracy in inference and sensitivity to data noise caused by neglecting gene association in an existing method are effectively solved.

The invention belongs to the technical field of wheat molecular breeding, and particularly relates to an SNP site associated with a wheat polyphenol oxidase active major gene. The SNP site has a polymorphic site of A / G allele mutation at the 36th basic group. In the application, the inventor tries to excavate and obtain key genetic loci and genes which can stably exist under multiple environment conditions and control the PPO activity of wheat on the basis of analyzing PPO activity characters of 207 representative wheat materials planted in different years by utilizing a whole genome association analysis method and combining a wheat 660K SNP chip; and the Whaas77577 molecular marker which is closely linked with the major gene qPPO3A.1 related to the PPO activity of the wheat is obtained. Based on the molecular marker, excellent allele variation type identification can be performed on breeding early generation materials, so that a scientific basis can be provided for wheat breeding progeny material selection.

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

This application provides a key gene network search method and system based on intelligent agents, belonging to the field of gene search technology. The method includes: acquiring multiple search data sets for searching key genes of target genes; for each search data set, scoring each gene in the whole genome based on the search data; determining a candidate gene set corresponding to the search data set from the whole genome based on the scoring results; performing set operations on the candidate gene sets corresponding to each search data set to obtain a target candidate gene set; for each target candidate gene in the target candidate gene set, treating the target candidate gene as a network node, and determining the node attributes of the network node based on the score of the target candidate gene in each candidate gene set; generating edges between each network node based on preset gene association evidence and each target candidate gene to obtain a key gene network. This application can improve the accuracy of key gene search.

This invention discloses an intelligent prediction system for lung cancer metastasis based on GCAVE-GAN and multimodal fusion. The system includes an image data augmentation module (GCAVE-GAN), an image feature fusion module, a gene feature extraction module, a multimodal fusion module (CFF), and a classifier module. The image data augmentation module uses a data augmentation network that measures the characteristic features of image-gene association. The gene data processing module uses feature selection and channel convolution techniques to extract gene markers related to lung cancer metastasis. The multimodal fusion module accurately captures the correlation between image and gene data through an adaptive multi-feature cross-attention mechanism, achieving deep information fusion and optimizing the prediction of lung cancer metastasis risk. This invention is particularly suitable for handling situations where the number of lung cancer images is insufficient, enabling rapid and stable assessment of lung cancer metastasis risk and significantly improving the accuracy and efficiency of the prediction model.

The embodiment of the invention provides a gene data processing method and device, a storage medium and electronic equipment, and belongs to the technical field of information analysis. The method comprises the following steps: acquiring a target gene set, wherein the target gene set comprises a plurality of first genes; querying a first number of common gene association relationships between any two genes in the plurality of first genes in a pre-constructed gene relationship knowledge graph, wherein the gene relationship knowledge graph comprises a plurality of association relationships between the plurality of genes and the plurality of functional entities; grouping the plurality of first genes based on the first number to obtain a plurality of gene analysis groups; and performing gene enrichment analysis on the plurality of genes contained in each gene analysis group according to the gene relationship mapping knowledge domain to obtain a plurality of gene enrichment analysis results. The method can improve the accuracy of enrichment analysis of gene data.