99 results about "Gene Feature" patented technology

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.

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 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 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 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.

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 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 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.

The application provides a carbon source intelligent screening method and device for strengthening microbial degradation of emerging pollutants, and belongs to the technical field of pollution control. The method adopts a carbon source screening model constructed and trained based on coupling of a large language model and machine learning, intelligently screens based on gene characteristics of microorganisms in a microbial community and candidate carbon source characteristics to obtain an advantage specific carbon source of functional microorganisms, and can screen the advantage specific carbon source for promoting growth of the functional microorganisms, so that the functional microorganisms are directionally enriched in the microbial community, the in-situ emerging pollutant degradation capacity of the microbial community is improved, and the treatment effect of the emerging pollutants is further improved.

Provided herein are compositions, systems, and methods for ranking cancer drugs for treating a subject's cancer cells, where a plurality of gene signatures (each with a plurality of gene signature genes) with associated cancer drugs are processed with raw mRNA expression levels for genes in the sample. The processing (e.g., by computer) can comprise: i) applying a normalization algorithm to generate normalized mRNA expression values for signature genes, ii) applying a median finding algorithm to the normalized mRNA expression values in each of the plurality of drug gene signatures to generate a plurality of median values, and iii) applying a ranking algorithm such that the median values are ranked from highest value to lowest value (or vice versa), with the highest value being associated with the most effective cancer drug, or most effective combination of two cancer drugs.

This invention provides a method, system, computer equipment, and medium for constructing a large-scale tea technology model, belonging to the field of agricultural informatization. The method includes acquiring tea images, tea tree gene sequences, tea technology knowledge text data, and molecular structure diagrams of tea compounds; training a Transformer model based on the tea knowledge information to obtain an initial model, extracting image features, text features, gene features, and molecular structures respectively; fusing features based on a cross-modal attention mechanism to obtain fused features; outputting human-readable text based on the fused features; extracting question-answer pairs from the tea knowledge information using prompt word templates through the initial model, constructing a tea knowledge question-answer pair dataset based on the question-answer pairs; training the initial model based on the tea knowledge question-answer pair dataset to obtain a large-scale tea technology model after fine-tuning. This method ensures the adaptability of the constructed model for application in the field of tea technology.

The application relates to a single cell recognition method and a storage medium, wherein the single cell recognition method comprises the following steps: obtaining single cell original gene expression data, a gene label control table and pre-training prior gene feature data; obtaining correction data based on the single cell original gene expression data, generating a cell supervision label, combining the gene label control table to construct a single cell gene set activity matrix; generating a training data set based on the above matrix, label, prior data and correction data, inputting the training data set into a classifier and a reconstruction module of an initial cell deep learning network, obtaining a classification result and a constructed activity matrix; obtaining a total loss by weightedly combining a label loss and a reconstruction loss, iteratively training a gradient in a reverse direction, and generating a cell deep learning network; inputting the correction data into the network, inferring a cell fusion embedding result, and further obtaining a single cell recognition result. Through the application, the problem of low single cell recognition accuracy is solved.

The invention discloses a method, a device, a medium and equipment for predicting low-temperature tolerance of saccharomyces cerevisiae, and relates to the technical fields of biotechnology, microbial engineering and computer. The method comprises the following steps: acquiring transcriptomics and metabonomics real-time data of saccharomyces cerevisiae; screening transcriptomics data, and reserving data with high inter-batch correlation and low variable coefficient; screening metabonomics data, and reserving data with small monitoring peak area and batch effect passing PCA (Principal Component Analysis) inspection; screening out gene characteristics of the transcriptomics data and metabolite characteristics of the metabonomics data according to the significance level and a logarithmic 2-time change threshold; processing the features, and integrating the features into a biomarker feature set; and inputting the feature set into a trained random forest model, and predicting a low-temperature tolerance classification result of the saccharomyces cerevisiae. According to the method, the capturing capability of the model on complex biological characteristics is remarkably improved, a multi-layer mechanism of low-temperature tolerance is disclosed, and the prediction precision of the low-temperature tolerance of the saccharomyces cerevisiae is effectively improved.

The application provides a test message derivation method and device, a storage medium and an electronic device. The method comprises the following steps: obtaining a target individual matched with a test requirement, the target individual containing gene characteristics matched with protocol information, the protocol information belonging to a network protocol corresponding to the test requirement, and each gene characteristic being used for indicating a derivation operation on the corresponding protocol information; obtaining target derived message data corresponding to the target individual, the target derived message data being derived message data obtained by performing a derivation operation on protocol information of original message data according to the gene characteristics contained in the target individual; and the target derived message data being used for testing a test system corresponding to the test requirement. The number of message data available for test training can be quickly enriched, the derived message data obtained can be used for test training of the test system corresponding to the test requirement, so that the stability and accuracy of the test system are improved.

The present invention provides a gene expression feature selection method, which relates to the fields of bioinformatics and machine learning. The method uses whether the joint mutual information between an original gene expression dataset and a gene expression dataset corresponding to a candidate feature subset and a category vector is equal as a judgment condition for maximum correlation, uses the maximum conditional mutual information formula as an objective function, iteratively selects gene features that meet the objective function to obtain a candidate feature subset with maximum correlation, and then deletes redundant features in the candidate feature subset to screen out a minimum feature subset with both maximum correlation and minimum redundancy.

The invention belongs to the field of network security, and particularly relates to a malicious code detection system based on an improved Siamese model, which comprises a gene extraction module, a gene pool management module, a Siamese network similarity calculation module, a detection decision module and a gene pool updating module. Performing similarity comparison on the extracted gene sequence and a known malicious code gene pool by utilizing an improved Siamese model to obtain a similarity calculation result; according to a similarity calculation result, judging whether the code file contains malicious gene characteristics or not, and if the malicious code is detected, further determining a malicious code family to which the malicious code belongs; and according to a detection result, updating and optimizing a malicious code gene pool, so that the malicious code gene pool covers the latest malicious code sample and variant features. Through combination of the malicious code gene features and the AI model, the effect of the malicious code detection task is remarkably improved.

The invention provides a gene feature selection method. Relates to the technical field of data mining. The method comprises the following steps: taking high-dimensional medical gene expression data to be analyzed as a search space; generating a population in the search space, and constructing an elite archive for storing a Pareto optimal solution; storing the Pareto optimal solution of the current population into an elite file; based on the difference between the occurrence frequency of the gene feature combination in the elite archive and the occurrence frequency of the gene feature combination in the current population as an information gain, selecting a high-quality gene feature combination in the elite archive to replace a non-high-quality gene feature combination in the current population so as to iteratively update the current population; when the number of times of continuous non-updating of the elite archive exceeds a stagnation threshold value, performing local redundant gene feature elimination operation and local gene feature replacement operation on the elite archive; judging whether a preset termination condition is met or not, and if not, returning to update; and if so, outputting the optimal gene feature combination in the elite archive. And gene feature selection with low gene number and high precision can be realized.

The invention discloses a bidirectional multilayer state space model for genome phenotype prediction. The bidirectional multilayer state space model comprises a bidirectional processing module, a multilayer state space model module and a decoding module, the bidirectional processing module is used for simultaneously extracting forward and reverse dependency relationships of a genome sequence and generating forward and reverse feature representations; the multi-layer state space model module is used for extracting local and global features of a genome sequence and enhancing the sequence modeling capability through a multi-layer structure; and the decoding module outputs a phenotype prediction result according to the feature representation generated by the multi-layer state space model module. According to the method, the super-long gene sequence can be efficiently processed, the calculation complexity is linearly increased, and the calculation and memory overhead is remarkably reduced; gene features are comprehensively extracted through a bidirectional multilayer structure, and the accuracy of phenotype prediction is improved; the end-to-end design further simplifies the process and enhances the applicability of the model.

A computer-implemented method for calculating an individual's tailored Polygenic Risk Score is based on known genetic information. A dataset is provided related to a reference panel including genetically characterized individuals with known disease status and diversified global ancestry. An individual-specific genetic reference group of individuals is selected as a subset from the reference panel. Genetic distances of the individual from each of the reference panel individuals are computed; each being the individual's genetic distance from a respective reference panel individual. Individuals of the individual-specific genetic reference group based on the individual's computed genetic distances are selected. The individual's basic Polygenic Risk Score for a disease is calculated to provide the individual's disease risk prediction. An ancestry-based background PRS contribution is determined. The ancestry contribution is removed from the individual's calculated basic Polygenic Risk Score to obtain the individual's tailored Polygenic Risk Score and provide a disease risk prediction.

The invention provides a method for predicting spatial gene expression, electronic equipment and a medium. The method comprises the following steps: acquiring a to-be-detected pathological histological image; extracting a first visual feature of the to-be-detected pathological histological image; converting the first visual feature into a first visual feature vector based on a preset projection model; based on the first visual feature vector, searching in a preset gene feature library according to a preset matching model to obtain a first gene feature vector matched with the first visual feature vector; and obtaining space gene expression of the to-be-detected pathological histological image based on the first gene feature vector.

The invention provides a feature editing method, device and equipment. The method comprises the following steps: acquiring an adjustment parameter of a gene feature; adjusting the gene features and acquiring prompt information of the gene features in real time; and when the content in the prompt information is matched with the adjustment parameter, ending the adjustment and forming a corresponding target gene feature. According to the method, the features can be adjusted very quickly, so that the feature adjustment efficiency and the user experience are improved.

The application relates to the technical field of biological information data analysis, and discloses an intelligent identification method and system for rapid genotyping diagnosis of gene polymorphism. 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, defining a multi-dimensional analysis space of sequence axes, function axes, variation axes and environment correlation axes; performing genotyping identification simulation based on the model to generate a preliminary genotyping strategy; and dynamically optimizing the preliminary genotyping strategy by using a self-adaptive feature selection algorithm to generate a multi-source collaborative genotyping strategy. By means of multi-modal data fusion and self-adaptive optimization, the application improves the efficiency and accuracy of genotyping diagnosis of gene polymorphism, can adapt to different populations and environments, optimizes resource utilization and reduces costs, and has important application value in the fields of biomedical research and disease diagnosis.