55 results about "Gene prediction" patented technology

Disclosed in the present invention are an end-to-end gene sequencing method, and a gene sequencer and a storage medium. The method comprises: acquiring fluorescence images to be tested that correspond to base signal collection units of a plurality of base types on a sequencing chip, wherein the fluorescence images to be tested comprise fluorescence images corresponding to the plurality of base types; on the basis of the fluorescence images to be tested, determining input image data to be tested; and using said input image data as an input for a trained deep learning gene prediction model, and the deep learning gene prediction model outputting a super-resolution feature map by means of a feature extraction network, performing base type recognition by means of a base type prediction network and on the basis of the super-resolution feature map, and outputting a basecall result, wherein the feature extraction network is obtained by means of performing training by using as labels super-resolution feature maps obtained by a trained super-resolution image model.

The invention provides a pathogenic driver gene prediction method based on biological network fusion and related equipment. The method comprises the following steps: acquiring data of various driver genes for training; constructing an initial gene relationship map based on protein interaction, gene sequence similarity, KEGG pathway co-occurrence, a gene co-expression mode and semantic similarity of a gene ontology, and embedding various human driven gene data for training into each node in the initial gene relationship map to obtain various gene relationship maps; performing dynamic adjustment on each gene relationship map through edge discarding, feature discarding and difficult sample recognition enhancement to obtain an adjusted gene relationship map for training the constructed pathogenic driving gene prediction model to obtain a trained pathogenic driving gene prediction model; inputting the target driver gene data into the trained pathogenic driver gene prediction model for prediction to obtain a prediction result; and the accuracy and robustness of pathogenic driver gene prediction are improved.

The invention discloses a hereditary ophthalmic disease intervention therapy prediction method. The method comprises the following steps: obtaining regulatory omics data of a hereditary ophthalmic disease, defining a core gene based on genetic evidence, defining a peripheral gene in combination with network evidence, performing calculation to obtain a gene-predictive factor matrix, and performing priority ranking on the genes; on the basis of the gene priority ranking list, a KEGG path is adopted to merge the network, and an intervention target network is identified and obtained; performing advanced analysis on the intervention target network, including lesion inhibition gene analysis, disturbance removal analysis and / or cross-disease priority atlas analysis, and identifying an intervention target with disease specificity; and predicting an intervention therapy for the hereditary ophthalmic disease according to the disease-specific intervention target.

The invention provides a solid tumor treatment target prediction method and system based on multi-modal omics data. The solid tumor treatment target prediction method comprises the following steps: inputting multi-modal omics data of a solid tumor, calculating an affinity score, and constructing a gene-predictive factor matrix; performing priority ranking on all the input genes, and performing function enrichment analysis on the preferentially ranked genes by using a KEGG pathway set; constructing a pathway intersection network related to solid tumor progression, and identifying a sub-network enriched with high-score nodes in the pathway intersection network by adopting a restart random walk algorithm; and based on drug research and development database information, drug reutilization analysis and disturbance removal analysis are carried out on the identified sub-network enriched with the high-score nodes. The invention discloses a method for realizing multi-modal omics data integration, analysis and identification of solid tumor treatment targets and drug reutilization combination, and aims to determine treatment candidate drugs including treatment targets and reutilization drugs.

The present invention belongs to the field of biomedicine, and specifically relates to a prognostic model for predicting the overall survival rate of AML patients based on 9 fatty acid metabolism-related genes. The present invention analyzed the gene expression profiles of 354 AML patients from TCGA and VIZOME, determined the prognosis-related genes based on the fatty acid metabolism-related genes of the TCGA dataset using univariate Cox regression analysis and survival analysis, and established a prognostic model with 9 fatty acid-related genes. This model can evaluate the prognosis of AML patients more specifically and sensitively. Then, using a nomogram, the fatty acid metabolism gene prognostic model, age, and cytogenetic risk degree were integrated into a scoring system to more accurately predict the survival of AML patients, with broad clinical application prospects.

The invention relates to a metagenome analysis method suitable for microbiota in a straw mushroom growth process, and relates to the technical field of microorganism application, and the metagenome analysis method comprises the following steps: extracting a microbial genome DNA sample in the straw mushroom growth process; detecting the quantity and the quality of the extracted DNA samples to obtain quantity and quality detection results of DNA; constructing a metagenome sequencing library with the insertion length of 400bp by using the extracted DNA samples, and sequencing to obtain original sequencing reads; the original sequencing reads are subjected to cutting treatment, and filtered reads are obtained; performing optimization processing on the filtered reads to obtain an optimization result; and performing gene data analysis on the optimization result, wherein the analysis content comprises gene prediction and gene abundance statistics. According to the method, the blank of microorganism detection in the straw mushroom growth process is filled up, and rich and comprehensive information analysis content is provided on the metagenome level.

The present invention belongs to the field of data mining technology and relates to a method for discovering the influence of key elements based on native-derivative topic transfer learning, including obtaining information including native topics and derived topics and related user information from an API interface provided by a social platform; constructing the early propagation network topology and propagation timing of derived topics, including using a joint distribution adaptive method to perform cross-domain feature adaptation on the content space of native topics and derived topics, and considering the sparsity of early data of derived topics, using an adversarial transfer learning method to compensate for the network structure; constructing a message-path-user ternary association graph of derived topics and performing cyclic iterative scoring to rank the influence of key elements of derived topics; the present invention can timely and accurately mine key elements in the early stage of the outbreak of derived topics, and the present invention can also be widely used in the precise placement of product advertisements, the discovery of important pathogenic genes, the prediction of popular research results, and the prevention of computer virus propagation.

The application discloses a gene editing system CRISPR-Cas12p and application thereof. Based on microbial genomes and metagenomic data, the CRISPR-Cas12p protein of the CRISPR-Cas protein family is obtained by preliminary screening by using a Prodigal gene prediction tool, a Pfam database and HMMER software, and a gene editing system CRISPR-Cas12p is constructed. PAM preference and interference function identification show that the editing system has a PAM preference of 5'-TTC-3', can effectively realize targeted cutting by using long transcripts and double RNA hybrid chain transcripts respectively, and can realize editing of a target gene in prokaryotic and eukaryotic cells. The CRISPR-Cas12p gene editing system obtained by the application has a small protein component, is beneficial to delivery, can realize gene editing in prokaryotic and eukaryotic cells, and has a wide application prospect.

The application relates to a method for predicting gene mutation based on deep learning using multi-modal slices, image block division is performed on a digestive tract HE slice, and based on the image block division result, each image block of the digestive tract HE slice is classified into a cancerous region; the digestive tract HE slice and an immunohistochemical slice are registered; based on the digestive tract HE slice and the registered immunohistochemical slice, a gene mutation prediction training sample set is constructed, a multi-instance learning model is trained, the training standard is whether a gene is mutated, and a gene mutation prediction model is obtained; gene mutation prediction is performed according to the obtained gene mutation prediction model, a model for predicting gene mutation in combination with multi-modal pathological data of a patient is used, high specificity of the model is ensured, compared with a pathological gene prediction model using single modal data, more modal data is introduced, and the prediction accuracy is effectively improved.

The invention relates to the technical field of gene detection, in particular to an opioid analgesic drug gene detection method based on multiple targeted amplification, which comprises the following steps: extracting DNA from a peripheral blood sample; the method comprises the following steps: setting a primer pool aiming at a preset site of a specific gene related to opioid drugs, and carrying out multi-targeted amplification sequencing by using the primer pool to obtain sequencing data; comparing the sequencing data with a human reference genome to obtain a data comparison rate, and judging the eligibility of the multi-target amplification sequencing process according to the data comparison rate; performing variation detection on the qualified sequencing data, identifying the base type of each SNP site, and determining the genotype of the subject in combination with a dbSNP database; and inputting the genotype and clinical data into a pre-trained multi-gene prediction model, and obtaining the medication guidance of the subject for using the opioid drugs. The gene detection efficiency is improved.

This invention provides a gene prediction and identification method, apparatus, device, and storage medium, belonging to the field of gene annotation and prediction. The method includes: acquiring the gene text to be annotated and a set of promoter-terminator pairs; preprocessing the gene text to generate a raw genome sequence; searching the raw genome sequence for base sequences that fuzzy search matches the set of promoter-terminator pairs to generate a base sequence to be aligned; and comparing the base sequence to be aligned with base sequences in a bioinformatics database based on the BLAST gene alignment method to generate gene prediction and identification results. This invention, by separating base sequences from promoter-terminator pairs and then performing BLAST gene sequence alignment, achieves coarse gene localization followed by BLAST gene sequence alignment, providing a method for predicting new genes, improving alignment efficiency and comprehensiveness, as well as the accuracy and comprehensiveness of gene prediction results.

Provided are a gene prediction method and apparatus, a computer device, and a computer readable storage medium. The method comprises: acquiring a template gene sequence, and a genetic gene sequence and a free gene sequence which correspond to a subject under test (step S102); determining a target gene site in the genetic gene sequence and the free gene sequence (step S104); extracting feature data corresponding to the target gene site, wherein the feature data is used for representing attribute features of the target gene site, and the feature data comprises first input data and second input data (step S106); acquiring a target gene prediction model, wherein the target gene prediction model comprises a first network, a second network, and a third network, and an output of the first network and the second network is an input of the third network (step S108); and respectively inputting the first input data and the second input data into the first network and the second network to obtain a gene prediction result outputted by the third network and corresponding to said subject (step S110).

The present application relates to the technical field of artificial intelligence and deep learning, in particular to a tomato salt tolerance prediction method and system based on deep learning, specifically as follows: obtaining a tomato salt tolerance genome, inputting it into a tomato salt tolerance prediction system based on deep learning for detection, sequentially passing through a salt-tolerant small sample gene recombination module, an epigenetic gene multi-granularity mining module, a heterogeneous salt-tolerant gene feature integration module, a salt-tolerant feature point fusion module and a tomato salt tolerance gene prediction module, and calculating the probability of having relevant tomato salt tolerance genes in the input tomato salt tolerance genome. The present application improves the prediction accuracy and generalization ability of salt tolerance genes under complex genetic background, and provides an intelligent prediction method for tomato salt-tolerant variety breeding.

The present invention discloses a method for cancer driver gene mining and interpretability analysis based on heterogeneous networks. The method comprises: constructing a multi-omics heterogeneous network and extracting initial node features through the multi-omics heterogeneous network; constructing an information transfer subgraph by random walk along a specific meta-path, and performing meta-path-based heterogeneous network feature extraction to obtain a representation vector of the gene node; inputting the representation vector of the gene node into a multi-layer linear classifier, using the multi-layer linear classifier to perform node classification, and outputting the analysis results of the cancer driver gene. The present invention calculates the representation vector within the meta-path through a network representation algorithm that includes multi-head attention and self-attention mechanisms, and calculates the contribution weight of each meta-path to the classification problem through global attention, thereby obtaining a more robust and effective cancer driver gene prediction result.

The invention provides a dairy cow ketosis regulatory gene prediction system based on multi-omics analysis and machine learning, which belongs to the technical field of molecular breeding and disease prevention and control, and comprises a data acquisition and preprocessing module, a feature set establishment module, a machine learning model establishment module and a result evaluation module, the method comprises the following steps: integrating dairy cow genome, transcriptome and metabolome data, screening candidate regulatory genes through whole genome association analysis, gene differential expression analysis, cis-eQTL positioning and co-positioning analysis, and constructing a gene expression feature set; core regulation genes are screened through L1 regularization penalty by means of a Lasso model, weights are distributed, model parameters are optimized in combination with grid search and cross validation, and model performance is evaluated through an ROC curve and an AUC value. According to the invention, an integrated technical system from gene screening to risk prediction is constructed, efficient screening of the core regulation gene and accurate prediction of ketosis risk are realized, and the economic loss of breeding is effectively reduced.

Provided are a gene prediction method and apparatus, a computer device, and a computer readable storage medium. The method comprises: acquiring a template gene sequence, and a genetic gene sequence and a free gene sequence which correspond to a subject under test (step S102); determining a target gene site in the genetic gene sequence and the free gene sequence (step S104); extracting feature data corresponding to the target gene site, wherein the feature data is used for representing attribute features of the target gene site, and the feature data comprises first input data and second input data (step S106); acquiring a target gene prediction model, wherein the target gene prediction model comprises a first network, a second network, and a third network, and an output of the first network and the second network is an input of the third network (step S108); and respectively inputting the first input data and the second input data into the first network and the second network to obtain a gene prediction result outputted by the third network and corresponding to said subject (step S110).

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

The present invention relates to a biomarker for predicting a treatment response to immunotherapy and a gene prediction model using the same. The gene prediction model using the biomarker is based on the expression pattern of a main gene by which characteristics of a tumor microenvironment of a patient can be recognized, and thus can predict a treatment response. Therefore, the gene prediction model makes it possible to avoid unnecessary surgery and determine an optimal treatment plan.

The present invention relates to a system for predicting transcription factor target genes based on tumor transcriptomic features, which consists of four major modules: a transcriptomic correlation calculation module at the pan-cancer level; a promoter region sequence acquisition module; a position weight matrix scoring module; and a transcription factor target gene screening module. Its advantages are as follows: It innovatively uses transcriptomic data at the pan-cancer level and utilizes the extensive and massive gene expression regulation network perturbations therein to achieve the purpose of simple and efficient prediction of transcription factor target genes in terms of operation; this system can obtain relatively accurate prediction results of transcription factor target genes by introducing position weight matrix (PWM) scoring; all the data and materials used in this system can theoretically be obtained through open public databases, which further reduces the experimental workload, technical and funding thresholds for researchers when using this system.

The invention provides an analysis method and system for virus metagenome sequencing data, and relates to the field of biological information.The method comprises the steps that quality filtering is conducted on original sequencing data, and filtered data is obtained; according to the filtered data, a sequence is assembled through a De Bruijn graph and an overlapping layout, and a virus metagenome sketch is constructed; comparing the virus metagenome sketch with a known virus database to identify known virus types; and identifying a new virus by combining the unknown sequence which is not compared with the virus sequence through clustering analysis so as to realize the sequence comparison of the virus metagene. By improving data quality and accuracy, enhancing virus identification and classification capability, improving variation detection and function annotation efficiency and optimizing gene prediction and community structure analysis, analysis of virus metagenome sequencing data is realized.

The application relates to the technical field of pathogenic gene prediction, and provides a pathogenic gene prediction method based on whole-transcriptome correlation research and related equipment. The method provided by the application comprises the following steps: calculating a splicing percentage matrix of each gene according to an RNA-seq sample; obtaining a genotype matrix of each gene and a genotype matrix of each splicing factor; constructing a first prediction model based on the genotype matrix of all splicing factors and all splicing percentage matrices, and obtaining a predicted splicing percentage matrix corresponding to all splicing factors by using the first prediction model; constructing a second prediction model based on the predicted splicing percentage matrix and the genotype matrix of all genes, and obtaining a final predicted splicing percentage matrix of all genes by using the second prediction model; and performing pathogenicity analysis according to the final predicted splicing percentage matrix of all genes to obtain a gene pathogenicity prediction result. The method provided by the application can improve the accuracy of pathogenic gene prediction.

The application discloses a method for eRNA transcription identification, regulation target prediction and function annotation based on high-throughput transcriptome sequencing data, and is characterized in that the method comprises the following steps: identifying part of non-coding RNA in which a transcription start site is located in an enhancer region as eRNA; obtaining eRNA-related protein coding genes which simultaneously exist in an eRNA-protein coding gene co-expression network and an eRNA-centered regulation network, constructing an eRNA-protein coding gene relationship network, and extracting protein coding genes which are directly connected or closely connected to the eRNA, so as to predict potential regulation targets of the eRNA; and finally, performing function enrichment analysis on the potential regulation targets of the eRNA, so as to obtain the results of eRNA function annotation, and the method has the advantages of wider application range, and can be applied to all eRNAs and more accurately obtain the action forms between the eRNA and the protein coding gene.

The invention relates to the technical field of biology, in particular to a cotton fiber initial key regulatory gene prediction method which comprises the following steps: collecting cotton ovule samples at different development time points for in-situ capture and transcription library construction and sequencing to obtain spatial transcription matrixes of different samples; performing data dimension reduction and clustering processing on the spatial transcription matrixes of different samples to obtain spatial slice information and UMAP dimension reduction images; according to the spatial slice information and the UMAP dimensionality reduction image, marking fiber-related and non-fiber-related cell groups, and extracting an SCT expression matrix; carrying out random stratified sampling based on the extracted SCT expression matrix, dividing the SCT expression matrix into a training set and a test set, and then training data of the training set by adopting a random forest algorithm to obtain a prediction model; and based on the prediction model and in combination with co-expression network analysis, obtaining an initial key regulation gene of the cotton fiber. By using the method, the initial key regulation gene of the cotton fiber can be quickly and accurately obtained.

The present invention provides a syndrome-gene relationship prediction method that integrates meta-path semantic dependency and transfer learning, belonging to the field of bioinformation processing technology based on deep learning. The present invention uses the dual-stream fine-tuning structure in deep transfer learning as the core framework. By establishing two transfer learning tasks: 1) the source domain is disease gene prediction; the target domain is syndrome gene prediction; 2) the source domain is symptom gene prediction; the target domain is syndrome gene prediction. Through training on the source domain task, the problem is transferred to the syndrome gene prediction problem in the target domain, realizing syndrome-gene relationship prediction in the target domain zero-sample scenario. In the main network of transfer learning, through our designed syndrome knowledge graph embedding learning, meta-path semantic embedding learning, and multi-order meta-path embedding aggregation, semantic dependency learning of relational meta-paths is realized. At the same time, a prediction scoring of relationships based on tensor decomposition is designed to realize prediction scoring of syndrome genes.

The invention discloses a method, a system, equipment, a medium and a program for predicting a disulfide death regulatory gene based on large-scale expression map correlation, and belongs to the technical field of disulfide death regulatory gene prediction. An expression residual error of a transcriptome gene expression matrix formed by converting human large-scale gene expression map data is calculated by using a PEER method, and the expression residual error is used for replacing a gene expression quantity, so that a whole genome gene expression residual error matrix is obtained. Then calculating expression correlation between each gene and a known disulfide death regulation factor; and taking the correlation of the disulfide death regulatory factors as a weight factor of weighted average correlation, and calculating the weighted average correlation of the whole genome gene and the known disulfide death regulatory factors to obtain the disulfide death characteristics of the whole genome gene. And the first 1% of the gene with the highest disulfide death characteristic is used as a potential disulfide death regulating gene. The method can predict the dithiodeath tendency of various cancer types and the difference of dithiodeath characteristics in different tissues and organs.

The invention relates to the field of bioinformatics, and discloses a space transcriptome gene expression prediction method and system, a terminal and a storage medium, and the method comprises the steps: segmenting a histological image into small blocks according to capture sites, obtaining the image, gene expression and position coordinates of each small block, and respectively learning gene features and image features, designing a loss function to capture potential correlation between the two modals, completing alignment between the two modals, and performing weighted aggregation on gene expressions of a plurality of gene features most similar to the image features to obtain corresponding predicted gene expressions. According to the method, the potential relationship between the histological image and the gene expression is captured, and the gene expression is predicted through the histological image by utilizing the potential relationship, so that the gene prediction accuracy is remarkably improved.

The application discloses an adaptive multi-channel graph neural network disease gene prediction model and a prediction method, and the method comprises the following steps: S1, through unified arrangement, standardized mapping and quality control of PPI network, disease seed genes and GWAS prior information, a candidate disease gene prediction input system for a low seed scene is established; S2, according to the candidate disease gene prediction input system for the low seed scene, a disease network representation and feature system for the low seed scene is constructed; S3, an adaptive multi-channel graph neural network model AdaMC-GNN for the low seed scene is constructed, and model parameters are updated and the model is trained; and S4, the disease network representation and feature of the low seed scene are input into the trained adaptive multi-channel graph neural network model AdaMC-GNN for the low seed scene, priority ranking of the candidate disease is obtained, and through disease level gating mechanism, local network propagation, cross-disease migration and GWAS prior evidence are fused, and whole genome ranking of sleep-related chronic disease candidate pathogenic genes is realized.

The invention relates to the technical field of virulence gene prediction, and provides a virulence gene prediction method based on whole transcriptome association research and related equipment. The method comprises the following steps: calculating a splicing percentage matrix of each gene according to an RNA-seq sample; obtaining a genotype matrix of each gene, and obtaining a genotype matrix of each splicing factor; constructing a first prediction model based on the genotype matrix of all the splicing factors and all the splicing percentage matrixes, and obtaining predicted splicing percentage matrixes corresponding to all the splicing factors by using the first prediction model; constructing a second prediction model based on the predicted splicing percentage matrix and the genotype matrix of all the genes, and obtaining a final predicted splicing percentage matrix of all the genes by using the second prediction model; and carrying out pathogenicity analysis according to the final prediction splicing percentage matrix of all the genes to obtain a gene pathogenicity prediction result. The method provided by the invention can improve the accuracy of virulence gene prediction.

The application relates to the technical field of gene analysis, and provides an Alzheimer disease gene prediction method and equipment based on alternative splicing, which comprises the following steps: performing difference analysis on the expression amount of all genes and the expression amount of transcripts to obtain expression amount difference characteristics, expression amount difference characteristics and expression proportion difference characteristics of the genes, and performing difference analysis on alternative splicing events of all sample gene signals to obtain alternative splicing difference characteristics of each gene; splicing the expression amount difference characteristics, the transcript expression amount difference characteristics, the expression proportion difference characteristics and the alternative splicing difference characteristics of each gene to obtain final characteristics of each gene; constructing a function correlation network according to the final characteristics of all genes; and performing gene disease prediction on the function correlation network to obtain the probability that each gene is related to Alzheimer disease. The method can improve the accuracy and reliability of Alzheimer disease gene prediction.

The invention discloses a gene prediction and functional peptide recognition method based on multi-feature fusion, and relates to a gene prediction and functional peptide recognition method. The invention aims to solve the problems of low efficiency and high cost caused by incapability of automatically extracting features in a traditional prediction method; most models depend on local sequence features and cannot fully capture potential biological activity signals of sequences, so that information is not fully considered. The system comprises a feature extraction module of a gene prediction part, a multi-feature fusion module, a convolutional neural network module, a Transform encoder module, a classification prediction module and a protein translation and storage module. The functional peptide recognition part comprises a peptide sequence representation module, a Bert module, a CNN module, a Bi-LSTM module and a feature fusion and classification module; constructing a data set; and evaluating the model. The invention belongs to the technical field of bioinformatics.