193 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 malicious code gene information recessive association mining method based on meta-learning, which comprises the following steps of: generating a function call graph (FCG) to obtain an attribute function call graph; training the feature extraction model based on the sequence-to-sequence mechanism by adopting meta-learning, and obtaining a key sub-graph of the function call graph from the generated attribute function call graph through the trained feature extraction model based on the sequence-to-sequence mechanism; generating a control flow graph (CFG), and obtaining an attribute control flow graph; using the optimized gene feature extraction model based on the graph structure for the generated attribute control flow graph to obtain a key sub-graph of the control flow graph; and a fine-tuning corpus is obtained through the key sub-graph of the function call graph and the key sub-graph of the control flow graph, and hidden association mining of malicious code gene information is carried out by using the fine-tuned large language model. According to the method, the recessive association of the malicious code gene information can be accurately mined, and the malicious code analysis precision and efficiency are remarkably improved.

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 relates to the technical field of metallogenic gene analysis, in particular to a metallogenic gene analysis method and system based on an expert knowledge base. The method comprises the following steps: acquiring authoritative theory information, analyzing the authoritative theory information, and determining a metallogenic gene feature information set; according to the metallogenic gene feature information set, carrying out directivity correlation analysis on the metallogenic genes, and determining a directivity node set and a target node set; constructing a metallogenic gene structure information base according to the pointing node set and the target node set; acquiring a mining area exploration data set, analyzing the mining area exploration data set based on the metallogenic gene structure information base, and determining an effective node information set; and based on the metallogenic gene structure information base, analyzing the effective node information set and the mining area exploration data set, and determining and outputting a metallogenic state analysis report. According to the method, the user can better perform resource management and decision making, sustainable mineral resource development and utilization are ensured, and scientificity and economical efficiency of mineral exploration are greatly improved.

The invention discloses a time sequence data alignment method and device, equipment and a medium, and relates to the technical field of artificial intelligence, the method comprises the following steps: using a feature extraction model and a pre-training language model to perform feature extraction on obtained time sequence gene expression data and text data, and further obtaining corresponding time sequence gene features and text features; and combining a target aggregation loss function and a training feature extraction model to obtain a trained feature extraction model aligned with different features so as to determine a final multi-modal model, thereby solving the problems that the requirements of time dynamic change and cross-modal semantic alignment are difficult to consider at the same time, a flexible mechanism is lacked to capture and integrate time sequence gene expression data, and the accuracy of the time sequence gene expression is improved. The technical problem that the multi-modal information fusion and alignment precision is poor is solved, and the technical effects of capturing the internal law and trend of data changing along with time, deeply mining semantic information and contextual relationships in texts, more comprehensively understanding and processing information and fully utilizing complementarity among different modal information are achieved.

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 belongs to the technical field of biological feature recognition, and relates to a gene text data amplification method based on a generative adversarial network, which comprises the following steps: acquiring original data, and converting the data into a format suitable for GAN processing; then constructing a GAN, inputting the processed data into the GAN for training, and generating a new gene sequence by using the trained GAN; the GAN comprises a generator, a discriminator and a feature discriminator; the generator is used for mapping random noise to a gene feature space and generating a new gene sequence similar to real data distribution; the discriminator is used for evaluating the authenticity of the generated data, and the feature discriminator is used for evaluating the consistency of the generated data and the original data in basic group distribution, so that the generated data is consistent with the original gene data in overall feature. The data generated by the method is close to real data in the overall structure, and is highly consistent in detail features, so that the reliability of the amplified data in practical application is ensured.

The invention relates to the field of multi-modal learning, in particular to a multi-modal fusion cancer lifetime prediction system and a storage medium, and a computer program in the storage medium executes the following steps: acquiring clinical data, a full-slice pathological image and gene data of a cancer patient; obtaining image feature data and gene feature data according to the full-section pathological image and the gene data; performing feature fusion on the image feature data and the gene feature data to obtain fused feature data; introducing a survival prediction classifier, and constructing a loss function by using the survival prediction classifier according to the fusion feature data, the image feature data and the gene feature data; and constructing a survival prediction model, training and evaluating the survival prediction model by using the clinical data, the full-slice pathological image and the gene data, and adjusting parameters of the survival prediction model through the loss function. According to the invention, the problem of inaccurate multi-modal prediction of the survival time of the patient is solved.

The invention relates to the technical field of medical informatics, and particularly discloses a construction method of a breast cancer lung metastasis prognosis prediction model. Comprising the following steps: (1) acquiring breast cancer in-situ and lung metastasis single cell RNA sequencing data by a GEO database, and preliminarily screening differential genes; (2) acquiring clinical information and gene expression profile information data of the breast cancer patient by a TCGA database, performing single-factor COX regression analysis, and screening genes related to the total survival rate; and (3) screening genes related to the total survival rate through LASSO regression to obtain six model genes, and calculating a risk scoring formula according to the expression quantity of the six model genes to complete the construction of the breast cancer pulmonary metastasis prognosis prediction model. According to the invention, scRNA-seq technical data from GSE202695 and a TCGA database are combined to develop a breast cancer lung metastasis prognosis prediction model based on six model gene characteristics of breast cancer lung metastasis.

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.

In some implementations, a machine learning model (e.g., a knowledge processing system) can apply a reasoning technique to knowledge representations associated with cells (e.g., using a genetic algorithm) and predict a clinical outcome (e.g., for a patient). In some implementations, a method includes producing a cell population. Gene expression data for the cell population can be received. The gene expression data can be analyzed to identify a set of gene signatures. The set of gene signatures can be provided as an input to a machine learning classifier. A predicted clinical outcome can be generated.

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.

Described herein are gene signatures providing prognostic, diagnostic, treatment and molecular subtype classifications of cancers through genomic and epigenomic profiling, including immune checkpoint regulators such as programmed death ligand 1 (PDL-1). Using methods and compositions described herein, specific and sensitive detection of biomarkers of interest is provided. Such biomarkers are indicative of disease pathogenesis, which provides opportunity for selection of treatment, including treatment regimes directed at overcoming resistance mechanisms.

Studies of non-human species are often pursued to validate or extend the biological insights of studies exploring gene-phenotype relationships in humans. When the results of non-human studies do not align with those of human studies, various questions arise regarding the study design, contextual factors, reagents and assays employed, and other related aspects. It is arguable that outside of the experimental conditions, there could be biological reasons why non-human and human studies produce discordant results exploring gene-phenotype relationships. These reasons might include the possibility that the gene in question has evolved a different function in one of the species or that the regulatory mechanisms governing its expression have undergone changes. This study assessed the potential of certain gene features, such as sequence conservation levels and aspects of regulatory architecture, to explain the concordant and discordant results obtained from studies involving humans and mice.

The present disclosure relates generally to methods for generating a pre-effector gene signature for determining the C cytotoxic effector potential of a preparation of chimeric antigen receptor (CAR) T cells comprising measuring single-cell gene expression data and endogenous T cell receptor (TCR) sequencing data of the preparation of CAR T cells. The present invention relates also to methods for determining the cytotoxic effector potential of a preparation of CAR T cells or a subset of CAR T cells therefrom as well as methods for treating a cancer in a subject in need thereof by administering a preparation of CAR T cells determined to have a cytotoxic effector potential.

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 relates to the technical field of computers, and discloses a nasopharyngeal carcinoma neoadjuvant chemotherapy sensitivity prediction method based on a neural network model, which comprises the following steps: screening tumor information of a historical nasopharyngeal carcinoma patient before and after neoadjuvant chemotherapy, and screening out differential genes; constructing a training data set based on the differential genes, calculating a connection weight between differential gene feature data of any two patients, constructing a loss function of the sensitivity prediction model, and training the sensitivity prediction model; after the sensitivity prediction model is trained, gene data of the patient is input into the sensitivity prediction model, and a prediction result that the patient belongs to the chemotherapy sensitive group or the chemotherapy resistance group is obtained. According to the method, the sensitivity prediction model is constructed by learning the mutual relation of differential gene characteristic data among nasopharynx cancer patients, and after the genes of the patients are input into the sensitivity prediction model, the model can predict the sensitivity of the patients to neoadjuvant chemotherapy according to various combination modes of the genes of the patients, and individualized treatment decision making is facilitated.

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.

The invention discloses a gene feature relationship generation method and device based on spatial contrast graph migration, relates to the technical field of image processing, and mainly aims to solve the problems of low generation efficiency and poor accuracy of an existing gene feature relationship. Comprising the steps that original expression data of a tissue gene are obtained, image enhancement is conducted on the original expression data through a graph converter, enhanced expression data are obtained, and the graph converter is constructed for a neural network model introducing a self-attention mechanism; respectively encoding the original expression data and the enhanced expression data through a variational auto-encoder, and respectively determining global distribution information and local distribution information of the original expression data and the enhanced expression data according to encoding levels, the variational auto-encoder is used for constraining encoding and decoding on the basis of a manifold structure; and under the condition that both the global distribution information and the local distribution information are aligned, generating a gene feature relationship of the tissue gene.