141 results about "Gene Feature" patented technology

The invention provides an intelligent management method and system for multi-source and multi-mode clinical research data, and relates to the technical field of data processing. The method comprises the following steps: acquiring an in-and-out standard of a subject and multi-modal clinical data of a patient; respectively vectorizing the text data, the image data and the gene data to obtain a text feature vector, a high-dimensional feature vector and a gene feature vector, so as to facilitate subsequent data integration; performing feature extension fusion on the text feature vector, the high-dimensional feature vector and the gene feature vector to obtain a fused feature vector, and effectively associating multi-modal data; according to the fusion feature vector and the in-arrangement feature vector of the in-arrangement standard, the matching difference degree is obtained, so that the multi-modal data play a mutual synergistic role when the matching difference degree is analyzed, and the matching difference degree is more accurate; according to the matching difference degree and a preset screening threshold value, the patients are screened, a list of potential subjects meeting requirements is obtained, and clinical researchers can make accurate decisions conveniently.

The invention provides a drug relocation method and a drug relocation system. The method comprises the following steps: predicting an expression profile of a drug after cell line disturbance according to a chemical structure of the drug, dose information of the drug and an undisturbed expression profile. And calculating the differential expression profile of the gene in the cell line based on the expression profiles before and after the cell line is disturbed by the drug. And for each drug, according to the differential expression profiles of the genes, calculating an average value of the differential expression profiles of the genes after the drugs disturb different cell lines, and sorting the genes based on the average value to obtain a sorting list of the differential expression profiles of the drugs. And according to the gene characteristics of the target disease and the sorting list, calculating the enrichment score of each drug on the target disease, and according to the enrichment score, evaluating the potential efficacy of the drug on the target disease. The drug relocation method provided by the invention can be used for giving disease specific gene characteristics for drug library screening.

The invention is applicable to the technical field of clinical medicine, provides a clinical multi-modal cancer drug response prediction method based on feature reconstruction, constructs a clinical multi-modal model for drug response prediction of diffuse large B-cell lymphoma, and aims to predict the drug response of diffuse large B-cell lymphoma by integrating gene sequencing and clinical multi-modal data. And accurate drug reaction prediction is realized. The model adopts an end-to-end multi-stage processing flow: firstly, extracting gene features through TransP-Net, and processing multi-modal clinical data by using a clinical information encoder; then, pseudo-gene features are generated through a clinical-genome filling module to deal with the data missing problem; and finally, multi-modal deep fusion is realized through a clinical information decoder, and a prediction result is output. According to the method, data characteristics and working processes in a real clinical environment are fully considered, two conditions of complete gene data and missing gene data can be processed at the same time, and the method has a good clinical transformation prospect and application value.

The invention provides an immunotherapy reaction marker identification method and system based on a cooperative game theory, and relates to the technical field of intelligent medical treatment, and the method comprises the following steps: obtaining immunotherapy single cell sequencing data, and determining a candidate gene set through differential expression analysis; extracting regulation and control relation pairs of the candidate gene set to obtain a multi-level information gene regulation and control network; embedding the multilevel information gene regulation and control network by adopting a Node2vec algorithm to obtain a network module containing potential biological functions, calculating the contribution degree of each gene feature to model prediction based on a Myerson value, and obtaining a feature importance sequence; combining the optimal features of the modules into a global candidate set; and iteratively optimizing the global candidate set, and outputting a target immunotherapy reaction marker set. Through a Myerson value principle in a cooperative game, a biomarker set related to immunotherapy response is efficiently searched, deep analysis of a drug resistance mechanism is realized, the accuracy and robustness of marker screening are improved, and the method serves for cancer clinical scheme customization.

The invention discloses a multi-mode chemotherapy response prediction system and method. A data set is based on pathological image data and gene data related to chemotherapy data; the first data processing module acquires a histopathological image data block through multi-parameter collaborative acquisition of chemotherapy patient pathological image slice segmentation and hole elimination; the second data processing module queries a gene data set according to gene expression standardization based on chemotherapy patient genes to obtain a first pathological gene feature vector; the third data processing module is used for processing the tissue pathological image data block based on an ImageNet pre-trained ResNet-50 model to obtain a first pathological image feature vector; the bilinear pooling module combines the first pathological gene feature vector and the first pathological image feature vector based on a cross-modal interaction method to obtain a fused pathological image gene feature vector; the attention clustering module performs package-level prediction on the fused pathological image features and gene features based on multi-instance learning to obtain patient chemotherapy response; the method can more accurately and reliably predict the potential response of the patient after chemotherapy.

A gene regulation inference method based on causal diagram embedding and conditional cellular network relates to the technical field of gene regulation network prediction, and comprises the following steps: 1, obtaining a gene expression matrix from scRNA-seq, and generating a causal diagram; 2, generating a local feature embedding matrix of a gene by using a graph neural network model based on a causal graph and a known gene regulation and control network graph; 3, constructing a CCSN based on scRNA-seq, converting the CCSN into gene connectivity vectors, and integrating the gene connectivity vectors of all cells to form a CNDM as a global feature matrix; 4, integrating the local feature embedding matrix and the global feature matrix to form a final gene feature matrix; 5, screening a core gene from the gene feature matrix, and constructing a regulation edge matrix; and 6, inputting the regulatory edge matrix into a gene link prediction module to realize inference of the gene regulatory network. By applying the method, the causal relationship and the cell specificity can be integrated, the core gene is effectively screened, the feature fusion is optimized, and the accuracy and the biological interpretation of network inference are improved.

The invention discloses a genetic state cancer clustering analysis method based on deep learning, which comprises the following steps: S1, constructing a single cell sequencing data set which comprises a gene expression matrix; s2, constructing a deep neural network composed of an input layer, a hidden layer and an output layer to obtain converted low-dimensional high-variation gene expression features; s3, constructing a gridding preprocessing model, and performing networked preprocessing including grid segmentation and isolated point removal on the low-dimensional high-variation gene expression features; constructing a clustering algorithm model, inputting the preprocessed low-dimensional high-variation gene expression features into the clustering algorithm model, and designing an optimization strategy based on a target function by the clustering algorithm model to enhance label entropy on a KL divergence index and amplify the ratio weight of auxiliary probability distribution and clustering distribution of the gene features; and iteratively realizing clustering result optimization as the output of the clustering algorithm model. Compared with the prior art, the clustering precision can be improved.

The invention discloses a tumor phenotype analysis method and system based on a disease source database and machine learning assistance, and relates to the field of artificial intelligence, and the method comprises the steps: obtaining clinical, gene and image multi-modal feature vectors of a tumor sample, inputting a multi-modal feature fusion network, and extracting each modal path vector; performing long-distance dependency modeling on clinical and gene features to obtain a first dependency feature map, and generating a second dependency feature map in combination with the feature association strength feature map; and determining a target tumor phenotype feature vector based on the second dependency feature map and the image feature path vector, finally calculating a matching confidence degree with a to-be-selected phenotype feature vector in a tumor phenotype database, and selecting a phenotype corresponding to the highest confidence degree as a target result. Through cross-modal feature dependence modeling and disease source database matching, the accuracy and efficiency of tumor phenotype analysis are improved, and support is provided for clinical precise diagnosis and treatment.

The invention discloses an automatic grading and grouping system and method for boars, and relates to the technical field of boar breeding, and the system constructs a multi-modal biological characteristic data set by collecting gene, semen quality, health examination and growth performance data. Secondly, extracting gene features by using a convolutional neural network, calculating a gene screening coefficient JYX, and classifying the gene screening coefficient JYX into an A class level and a B class level through a first threshold Q1; then, semen quality analysis is conducted on the A-class boars, a semen quality screening coefficient ZLX is calculated, and the A-class boars are divided into the A1-class boars and the A2-class boars through a second threshold Q2; and when the A2-class boar triggers health early warning, further evaluating a health index JCZ, and subdividing the health index JCZ into A21, A22 and A23 classes according to a third threshold Q3. And finally, grouping the boars according to the class levels of the boars, and formulating an accurate feeding strategy to optimize the reproductive performance and improve the breeding benefits.

The invention discloses a tumor identification method based on multi-modal deep learning. The method comprises the following steps: S1, collecting multi-modal data, and respectively forming an image feature matrix and a gene feature matrix; and S2, inputting the image modal feature matrix into a convolutional neural network model encoder to obtain image feature representation, and inputting the gene modal feature matrix into a multistage progressive embedded encoder to obtain gene feature representation. And S3, designing a mode collaborative attention fusion mechanism which comprises a cross-mode attention mechanism and gating residual connection and is used for generating fusion feature representation. And S4, classification prediction: inputting the fused feature representation into a designed classification model, and outputting a corresponding tumor category prediction label. According to the tumor identification method provided by the invention, the accuracy of feature fusion expression and the classification boundary distinguishing capability of the classifier can be improved, and the accuracy and robustness of tumor identification are further improved.

The invention relates to the technical field of medical data analysis, in particular to a lung adenocarcinoma osimertinib drug resistance prediction model construction method and system based on machine learning. Clinical data of lung adenocarcinoma patients and mRNA (messenger ribonucleic acid) data of a public database are integrated, after Mann-Wh itney U inspection, FPKM standardization and other preprocessing are carried out, XGBoost is utilized to construct a basic model driven by clinical indexes, a prognosis model driven by gene characteristics is constructed through R packet Mime, and TYMS and UAP1L1 are screened out to serve as key genes. The performance of the model is optimized through residual correction and stacking integration, and finally the AUC is improved to 0.924. Serum RNA (Ribonucleic Acid) detection and verification show that TYMS and UAP1L1 are remarkably and highly expressed in a drug-resistant group. According to the method, traditional biopsy is replaced with noninvasive serum detection, the detection cost is reduced, the model generalization ability is verified through an independent queue, and an efficient and accurate solution is provided for early warning and personalized treatment of osimertinib drug resistance of the lung adenocarcinoma patient.

The embodiment of the invention discloses a coprophilous fungus transplantation donor and acceptor dynamic matching method, device and equipment and a medium, and the method comprises the steps: responding to a donor and acceptor matching request, and obtaining the monitoring data of a to-be-matched acceptor after coprophilous fungus transplantation at the current preset sampling time; wherein the monitoring data after coprophilous fungus transplantation is determined on the basis of the coprophilous fungus transplantation operation of a to-be-matched receptor completed by the coprophilous fungus flora of any target donor, and the target donor is determined by performing adaptability matching on the gene feature information, the immune feature information and the clinical feature information of the to-be-matched receptor and a plurality of to-be-selected donors; and inputting monitoring data after coprophilous fungi transplantation into the transplantation strategy control model, outputting at least one of a donor switching strategy and a dose adjustment strategy for adjusting a to-be-matched receptor in a next time period, realizing comprehensive compatibility evaluation through multi-modal data integration, continuously optimizing a treatment scheme by means of a dynamic feedback mechanism, and improving the compatibility of the to-be-matched receptor. And the accuracy and adaptability of coprophilous fungus transplantation treatment are remarkably improved.

The embodiment of the invention provides a gene data processing method and system, electronic equipment and a storage medium, and belongs to the technical field of gene analysis. The method comprises the following steps: obtaining genotype data of a to-be-detected individual, and determining a plurality of associated genes of the to-be-detected individual according to the genotype data and single nucleotide polymorphism data associated with a target risk task; querying associated gene features corresponding to each associated gene in a pre-constructed gene feature library, wherein the gene feature library comprises embedding representations of different genes constructed based on an embedding model; the embedded model is obtained by performing self-supervised learning on a plurality of gene expression profiles of the same species as the individual to be detected; carrying out feature integration on the plurality of associated gene features to obtain individual features; and calling a prediction model to process the individual features to obtain a risk score of the to-be-detected individual corresponding to the target risk task. The method can improve the accuracy of gene data processing.

The invention relates to a double-gene rare variation and disease relevance prediction model and an establishment method and application thereof, and belongs to the technical field of biological medicines.The establishment method of the double-gene rare variation and disease relevance prediction model comprises the following steps that S1, a sample library is screened; s2, performing quality control on whole exome sequencing data (WES); s3, performing phenotype screening; s4, performing grouping design; s5, carrying out PheWAS logistic regression analysis; s6, performing Firth logistic regression analysis and verification; and S7, carrying out double-gene feature analysis and double-gene pathogenicity relevance prediction. The method for analyzing the correlation between the rare double-gene variation and all disease phenotypes is designed for the first time, and a new method is provided for screening hereditary pathogenic factors of various diseases.

The invention relates to the technical field of biological information data analysis, and discloses an intelligent identification method and system for gene polymorphism rapid typing diagnosis. The method comprises the following steps: acquiring multi-source gene sequencing data and preprocessing to generate a structured gene feature set; constructing a multi-modal data fusion model, and defining a multi-dimensional analysis space of a sequence axis, a function axis, a variation axis and an environment association axis; performing typing recognition simulation based on the model to generate a preliminary typing strategy; and dynamically optimizing by adopting a self-adaptive feature selection algorithm to generate a multi-source collaborative typing strategy. Through multi-modal data fusion and self-adaptive optimization, the efficiency and accuracy of gene polymorphism typing diagnosis are improved, the method can adapt to different crowds and environments, resource utilization is optimized, cost is reduced, and the method has important application value in the fields of biomedical research, disease diagnosis and the like.

The invention discloses a gene feature extraction method in gene data based on sequence search, and relates to the technical field of biological information. The problems that gene data is wide in source and diversified in format, data processing is extremely complex, data of different formats are difficult to integrate and analyze, and the accuracy and efficiency of feature extraction are affected are solved. According to the method, gene data are comprehensively preprocessed, low-quality and redundant parts are removed, reliable data quality is ensured, data distribution and data conversion are performed at the same time, high-similarity gene feature regions are accurately screened through sequence comparison, importance weights of gene features are determined by using clustering group features, key features are reasonably screened, and the accuracy of gene feature extraction is improved. Data dimensions are effectively reduced, the feature extraction efficiency and accuracy are improved, local and global features are fused to construct a comprehensive feature vector, the feature expression ability is enhanced, a screening result is verified by constructing a test data set, and the accuracy and reliability of the gene feature extraction method are continuously improved.

The invention provides a spatial domain identification method based on artificial intelligence, which belongs to the field of spatial domain identification, combines a traditional graph convolutional network with multiple modals, learns potential representations of data of different modals, and pre-processes and standardizes gene expression data and histological image data to obtain gene features and image features. The method comprises the steps of generating a spatial neighborhood graph and a spatial adjacency matrix based on spatial coordinate information, constructing a cross-modal graph convolution network model, reconstructing feature vectors and performing clustering after multi-view graph convolution, a self-attention mechanism with pruning operation and cross-modal joint embedded learning module learning, generating a spatial clustering graph, and evaluating a clustering effect by using an evaluation index. According to the method provided by the invention, different representations can be ensured to have consistency in space, and the robustness of the model is improved.

The invention provides a data analysis method in a high-value medical data asset construction process, and relates to the technical field of medical data analysis, and the method comprises the following steps: generating a gene feature subset, a clinical feature subset and an image feature subset; performing time dimension coding on the gene feature subset to generate a gene expression time sequence vector; marking timestamps for the clinical feature subsets, and extracting clinical event time sequence features; carrying out three-dimensional space registration on the image feature subset, and extracting space-time dynamic features of the focus area; based on the spatio-temporal dynamic features, image focus coordinates are obtained, and the image focus coordinates are mapped to a gene-clinical united space; constructing a heterogeneous graph network comprising gene nodes, clinical nodes and image nodes, and calculating edge weights among the nodes; in the heterogeneous graph network, redundant edges are cut off, a space-time correlation graph is generated, and then a correlation mapping matrix is obtained. According to the method, heterogeneous data are integrated, so that data originally dispersed in different systems can establish an effective relation.

Described herein are gene features that provide prognosis, diagnosis, treatment and molecular subtype classification of cancer by genomic and epigenomic profiling, including immune checkpoint regulators such as Programmed Death Ligand 1 (PDL-1). Using the methods and compositions described herein, specific and sensitive detection of biomarkers of interest is provided. Such biomarkers indicate disease pathogenesis, which provides opportunities for selection of treatments, including treatment regimens intended to overcome tolerance mechanisms.

The present invention discloses a method for predicting adaptive immune receptors by integrating gene and sequence information, comprising the following steps: S1, modifying the SC-AIR-BERT model to construct an SC-AIR-BERT-Multi model; the SC-AIR-BERT-Multi model comprises a gene information extraction channel, a sequence information extraction channel, a multimodal fusion module, and two multi-layer perceptrons for multi-task learning; S2, in the gene information extraction channel, using the gene name as input, obtaining the gene feature h of the immune cell receptor gene ; S3, in the sequence information extraction channel, using TCR sequence or BCR sequence as input, obtain the sequence feature h of immune cell receptor seq ; S4, the gene features and sequence features of the V, D, and J gene segments are sent to the multimodal feature fusion module for fusion, and the fused features are generated; S5, the multimodal receptor feature Representation learned in step S4 is mapped to the final TCR or BCR antigen binding specificity prediction and affinity prediction results through two multi-layer perceptrons for prediction.

The invention discloses a mitochondrial molecular marker for identifying acrossocheilus fasciatus from different water systems and application of the mitochondrial molecular marker. A primer kit of the acrossocheilus fasciatus mitochondrial molecular marker comprises a primer pair as shown in SEQ NO: 1 and SEQ NO: 2, and / or a primer pair as shown in SEQ NO: 3 and SEQ NO: 4; the acrossocheilus fasciatus mitochondrial molecular marker is formed by amplifying primers contained in the acrossocheilus fasciatus mitochondrial molecular marker primer kit. The identification method comprises the following steps: carrying out DNA extraction on acrossocheilus fasciatus to be identified; carrying out PCR (Polymerase Chain Reaction) amplification on the extracted DNA target fragment by using the primer pair; sequencing the PCR amplification product to obtain a base sequence, comparing the base sequence with the acrossocheilus fasciatus mitochondrial molecular marker, and identifying the water system source of the acrossocheilus fasciatus to be identified according to the specific site of the acrossocheilus fasciatus. According to the method, acrossocheilus fasciatus geographical populations distributed in different water systems are distinguished from gene features, and technical guarantee is provided for acrossocheilus fasciatus proliferation and releasing parent sources and offspring seed traceability.

The invention relates to the technical field of medical treatment, and discloses a lung cancer personalized treatment scheme recommendation method and system and a storage medium. The method comprises the following steps: acquiring clinical and molecular indexes of a patient, and constructing a simplified feature set; distributing weights for treatment targets according to the simplified feature set, constructing and optimizing a scheme evaluation matrix, and generating a preliminary scheme sorting list; through threshold screening and patient feature matching degree verification, a verified scheme set is obtained; the schemes are classified based on gene mutation and driver gene features, and classified optimization scheme subsets are obtained; constructing an interaction model to analyze interaction influence of toxic and side effects and life quality on curative effects, and dynamically adjusting scheme scores; and if the score is lower than a threshold value, triggering iterative optimization, obtaining a scheme list after iteration, and further determining an optimal treatment scheme according to treatment collaboration and target balance. According to the method, intelligent and closed-loop optimization from multi-source data to personalized treatment decision is realized, and the personalization and accuracy of a treatment scheme are improved.

The invention relates to the field of medicine and the field of machine learning, in particular to a gene feature recognition method based on comparative learning and related equipment thereof. The method comprises the following steps: firstly, acquiring a target gene feature vector, and inputting the target gene feature vector into a pre-trained ovarian cancer gene feature recognition model, so that the ovarian cancer gene feature recognition model outputs a corresponding gene feature recognition result; wherein the ovarian cancer gene feature recognition model is obtained through training by the following steps: acquiring a plurality of sample gene data; performing characterization processing on each sample gene data to obtain a corresponding sample feature vector; performing feature space mapping on the plurality of sample feature vectors to obtain a remapped feature space; constructing a decision forest model based on the remapping feature space; and performing optimization iteration on the model parameters of the decision forest model to obtain the ovarian cancer gene feature recognition model. According to the invention, the accuracy of ovarian cancer gene feature recognition can be improved.

The invention discloses a medicinal plant phenotype prediction method based on a gene action mode, which comprises the following steps of: firstly, constructing a medicinal plant phenotype prediction model based on the gene action mode according to an influence mode of a gene on phenotype, and introducing Gaussian noise and sparse regularization loss to extract key gene characteristics aiming at the influence of the key gene on the phenotype; meanwhile, a hidden layer is used for carrying out data dimension reduction to ensure the model efficiency, gene information of different scales is used for enriching gene feature representation, the inhibition effect between genes is extracted through self-attention, the nonlinear synergistic effect of the genes and the genes is extracted through polynomial features, and finally accurate prediction of phenotypic characters is achieved. According to the method disclosed by the invention, phenotype prediction is carried out from interaction between key genes according to an action mode of a genome on phenotypes, and compared with an existing phenotype prediction technology, the method disclosed by the invention has a better effect in phenotype prediction of medicinal plants, realizes rapid and accurate prediction of crop phenotypes, and accelerates breeding and seed production processes.

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 present invention discloses a method, device, terminal, and medium for predicting synthetic lethal gene pairs based on a graph convolutional neural network. The synthetic lethal gene pair prediction method includes: obtaining protein structural features based on protein structure data; obtaining protein sequence features based on protein sequence data; obtaining protein functional features based on a protein-protein interaction network; merging and standardizing the protein structural features, sequence features, and functional features to obtain the gene features of the primary protein-producing genes; obtaining interactions between genes, and training a synthetic lethal gene pair prediction model based on a graph convolutional neural network using the gene interactions and gene features; obtaining a final feature representation for each gene based on the trained synthetic lethal gene pair prediction model, and predicting whether two genes form a synthetic lethal gene pair based on the final feature representation. This method improves the efficiency of feature extraction and the ability to predict gene interactions.

The invention belongs to the technical field of cell data processing, and relates to a single cell gene characterization system and method based on deep learning. The system comprises a gene information input module, a data processing module, a context sensing interpolation module, a gene feature fusion module, a deep representation learning and joint optimization module and a cell typing and differentiation track inference module. The input module integrates a data source; the processing module preprocesses and enhances the original expression matrix; the interpolation module performs weighted interpolation by using a conditional Gaussian graph model; the gene feature fusion module generates a gene module activity matrix through 1 * 1 convolution and the like; the fusion module fuses the sample meta-information with the active matrix, the sequencing depth and the gene feature vector; the depth representation module captures high-dimensional dependence through a multi-layer Transform network, and generates cell high-order potential representation; the typing module outputs a cell population division and differentiation trajectory based on the deduced pseudo-time trajectory. The method has the beneficial effects that cell expression characteristics are recovered under high sparseness and noise, and a new way is provided for cell type identification and the like.

The invention relates to the field of biological detection, and discloses a method for analyzing prognosis gene characteristics of glioma based on machine learning. The method comprises the following steps: acquiring prognosis gene expression data and pathological variable data corresponding to a glioma evaluation sample; determining a prognostic gene score of the prognostic gene expression data based on a pre-constructed prognostic scoring model; and analyzing the prognosis risk of the neuroglioma evaluation sample according to the prognosis gene score and the pathological variable data to obtain a prognosis risk analysis result. The problems that an existing neuroglioma prognosis evaluation method is insufficient in accuracy and lacks a quantitative analysis model are solved.

A method for identifying cell subpopulations associated with disease phenotypes, belonging to the biomedical field. To identify cell subpopulations associated with disease phenotypes, this invention collects single-cell RNA sequencing data of the disease to obtain a single-cell expression matrix, collects the bulk expression matrix of the disease and corresponding phenotypic tags, and downloads human protein-protein interaction data to construct a protein-protein interaction network; extracts gene signature features of cells and samples and maps them to the protein-protein interaction network to form corresponding cell modules and sample modules; calculates the distance between each cell module and each sample module, and determines a set of multiple sample modules as the sample module set of the disease phenotype; calculates the distance between cell modules and the sample module set of the disease phenotype; creates a background distance distribution to evaluate the statistical significance of the distance between cell modules and the sample module set of the disease phenotype, and identifies cells whose distance to the sample module set of the disease phenotype is significantly smaller than the background distance distribution.

The embodiments of the present application provide a method, system, electronic device and storage medium for processing genetic data, which belongs to the field of genetic analysis technology. The method obtains the genotype data of the individual to be tested, and determines multiple associated genes of the individual to be tested based on the genotype data and the single nucleotide polymorphism data associated with the target risk task; queries the associated gene features corresponding to each associated gene in the pre-constructed gene feature library, and the gene feature library includes embedded representations of different genes constructed based on the embedding model; the embedding model is obtained by self-supervised learning of multiple gene expression profiles of the same species as the individual to be tested; integrates the features of multiple associated gene features to obtain individual features; calls the prediction model to process the individual features to obtain the risk score corresponding to the individual to be tested and the target risk task. This method can improve the accuracy of genetic data processing.