23 results about "Gene recognition" patented technology

The invention belongs to the technical field of gene identification, and discloses a method for integrating multi-omics data to enhance genome prediction and candidate gene identification, candidate gene identification is verified through multi-layer evidence integration, and the verification comprises priority ordering based on gene contribution scores, function enrichment analysis, generic genome network verification and CRISPR / Cas9 experimental verification. Evaluation on a corn population (n = 174) containing complete genomics, transcriptomics, translational omics and proteomics maps shows that the framework is remarkably improved in grain character prediction and is improved by 2.9-12.3% compared with a genome selection baseline, and meanwhile candidate genes verified by experiments are recognized. The invention further verifies the universality of the framework to five traits on an arabidopsis thaliana population, and provides an open source software platform to promote the practical application of the framework in a breeding plan.

The invention provides a spatial transcriptome tissue specific expression gene identification method and system based on a multi-dimensional statistical index, and belongs to the technical field of gene identification, the method comprises the following steps: obtaining gene expression data corresponding to a plurality of genes in different cells, different cells belonging to different types of tissues; the gene expression data is data processed based on a space transcriptome; for each gene, determining a basic statistical feature and a differential expression feature corresponding to the gene based on the gene expression data corresponding to the gene in different cells; determining specific index characteristics of the gene based on the basic statistical characteristics of the gene; and determining a tissue specific expression gene from the plurality of genes based on the basic statistical characteristics, the differential expression characteristics and the specific index characteristics corresponding to each gene. According to the invention, the accuracy of tissue specific expression gene recognition can be improved.

The application discloses a landscape gene identification method based on machine learning, relates to the technical field of machine learning and data science, and comprises the following steps: constructing a multi-level landscape gene classification architecture, pre-training a machine learning model based on the multi-level landscape gene classification architecture, and obtaining a landscape feature identification model; then, acquiring landscape element data corresponding to a to-be-identified landscape, calling the landscape feature identification model to analyze the landscape element data, and obtaining a landscape index identification result; then, taking the landscape index identification result as the basis, acquiring a first landscape gene identification result by using a feature deconstruction method, acquiring a second landscape gene identification result by using a prototype-variation theory, and acquiring a third landscape gene identification result by using a digital analysis strategy; and finally, taking the three results as a target landscape gene identification result, so that the efficiency and accuracy of landscape gene text identification and extraction are improved, and the application of the landscape gene is facilitated.

The invention discloses a cancer driver gene identification method based on conflict perception Markov blanket discovery, which comprises the following steps: S1, carrying out a preliminary condition independence test, screening genes related to cancer phenotypes, and obtaining an initial candidate father-child node set ICPCT; s2, detecting independent or dependent behaviors of genes in the initial candidate father-child node set ICPCT under different condition sets, and screening to obtain a candidate father-child node set CPCT and a candidate mating node set CSPT; s3, performing multi-condition pruning operation on genes in the candidate partner node set CSPT, and obtaining a partner set SPT after screening; s4, performing father-child node filtering operation based on conflict perception on genes in the candidate father-child node set CPCT, and obtaining a father-child set PCT after screening; and S5, integrating the mating set SPT and the father-child set PCT to obtain a Markov blanket of the target gene T, and outputting the Markov blanket as a cancer driver gene set.

The invention belongs to the technical field of bioinformatics and genomics, and discloses a spatial multi-omics enhancer and gene regulation relationship identification method, which comprises the following steps of: acquiring spatial multi-omics data of the same tissue slice, preprocessing the data, constructing a layered linear space model according to the preprocessed data, and constructing an enhancer and gene regulation relationship between the enhancer and the gene regulation relationship between the enhancer and the gene regulation relationship between the enhancer and the gene. Removing spatial autocorrelation based on a hierarchical linear space model, then performing parameter estimation of the model, finally performing significance test according to a parameter estimation result to obtain a gene significantly regulated by an enhancer, and identifying a gene and enhancer pair having a significant regulation relationship based on the gene; the identification method disclosed by the invention can accurately and reliably identify the regulation and control relationship between the real enhancer and the gene in the spatial multi-omics.

A traditional Chinese medicine intelligent screening method based on network targeting comprehensive indexes belongs to the technical field of traditional Chinese medicine intelligent screening. The method comprises the following steps: constructing a target set A and a protein interaction network database corresponding to each traditional Chinese medicine; constructing a disease core gene set B based on a protein interaction network; calculating a network targeting comprehensive index based on the target point set A and the disease core gene set B; calculating the network targeting comprehensive index of each traditional Chinese medicine and then performing ascending sorting, wherein the smaller the network targeting comprehensive index is, the stronger the network targeting relevance between the traditional Chinese medicine and the disease is; and matching the traditional Chinese medicines according to the obtained ascending order, and generating a structured and explainable final recommendation report. According to the method, the disease-related gene identification accuracy is improved, the biological interpretation of the result is enhanced, the standardized processing of integrating multi-source data into intelligent recommendation is realized, and the urgent demand of efficient and accurate screening in modern research of traditional Chinese medicines is met.

This application relates to the field of gene recognition technology, and provides a method and related equipment for identifying pathogenic genes based on abnormal expression. The method includes: for each target gene, calculating tail enrichment statistics in two tail directions based on the grouping tag values ​​of all signal vectors to characterize the abnormal expression of the target gene at the low or high expression end; determining the optimal tail enrichment direction from the two tail directions based on the two tail enrichment statistics; calculating the tail boundary shift degree of the target gene according to the optimal tail enrichment direction; calculating the significance p-value of each target gene; identifying multiple pathogenic genes from all target genes based on all significant p-values; and performing pathogenicity analysis based on the tail boundary shift degree and optimal tail enrichment direction of all pathogenic genes to obtain the pathogenic gene identification result. The method of this application can improve the accuracy of pathogenic gene identification.

The invention belongs to the technical field of microbiological detection, and particularly relates to a rapid detection method of pseudomonas aeruginosa RAA-CRISPR / Cas12a and application of the rapid detection method of pseudomonas aeruginosa RAA-CRISPR / Cas12a. According to the invention, based on the combination of recombinase polymerase amplification (RAA) and a CRISPR-Cas12a (clustered regularly interspaced short palindromic repeats-CRISPR-Cas12a) system, the ultrasensitive and rapid detection of the Pseudomonas aeruginosa is realized through constant-temperature nucleic acid amplification and specific gene recognition. The detection limit of the method for detecting the pseudomonas aeruginosa is 5.37 * 10 < 1 > copies / L (genome DNA) or 6.4 * 10 < 1 > CFU / mL (bacterial liquid); the detection time is only 21 minutes, and the method can be applied to rapid detection of various environmental samples.

The invention discloses a human organ aging evaluation and analysis method, equipment, a medium and a product, and relates to the field of aging biology and bioinformatics, the method comprises the following steps: based on multi-time-point transcriptome data, carrying out gene identification and screening to obtain aging trend genes; on the cross-organ level, the aging trend genes in different organs are subjected to confluence analysis, and the genes showing consistent up-regulation or consistent down-regulation trends in at least n organs are extracted as global aging trend genes; obtaining an organ aging original score based on the global aging trend gene by adopting the constructed multi-organ aging evaluation model; performing standardization processing on the organ aging original score; and transversely comparing standardized global aging trend gene scores of different organs at the same time point to obtain an aging degree quantitative evaluation result of each organ. The molecular regulation rule of organ aging can be deeply disclosed, and system integration and application of multi-omics data in aging biological research are promoted.

The application discloses a plant salt-alkali-resistant gene recognition method and system, in particular to a plant salt-alkali-resistant gene recognition method and system based on machine learning, which aims at solving the problem of low accuracy of plant salt-alkali-resistant functional gene recognition caused by high false positive rate and false negative rate of the recognition result of the salt-alkali-resistant gene recognition method which depends on the recognition of plant homologous genes, and comprises the following steps: obtaining a plurality of plant protein sequences known to be salt-alkali-resistant genes or not; obtaining a feature vector of the plant protein sequence; constructing a C4.5 algorithm model, training the C4.5 algorithm model with the feature vector, outputting whether the gene is a salt-alkali-resistant gene, and obtaining the trained C4.5 algorithm model; executing S2 on the plant protein sequence to be recognized to obtain a feature vector, inputting the feature vector into the trained C4.5 algorithm model, and obtaining whether the plant protein sequence to be recognized contains a salt-alkali-resistant gene. The system executes any step of the method. The application belongs to the field of gene recognition.

The invention discloses an information processing method, device and equipment for individual prognosis gene recognition and a medium, relates to the field of artificial intelligence and biomedicine crossing, is applied to a computer device, and comprises the following steps: screening out undetermined genes belonging to a shared causal gene set from a cross-section gene sequencing sample of a current object; the shared causal genes in the set are genes obtained by performing survival distribution simulation and independence test on cross-section genes and survival data of historical objects to eliminate hybrid associated genes; inputting the actual expression quantity of the to-be-determined gene into the target gene expression prediction model to output the expected expression quantity of the to-be-determined gene, and determining the difference between the actual expression quantity and the expected expression quantity of the to-be-determined gene as the residual error of the to-be-determined gene; and comparing the total gene expression residual distribution with the residual of the to-be-determined genes so as to identify the individual prognosis genes of the current object exceeding a preset group normal fluctuation range from the to-be-determined genes. And completing accurate individual prognosis gene identification suitable for each group on the basis of the cross-section gene data.

The invention discloses an intelligent autumn pear whole genome association analysis system based on deep learning, and relates to the technical field of gene deep learning, which comprises the following steps: collecting genotype data and phenotype data of autumn pears, obtaining a standardized data set through preprocessing, converting the standardized data set into a matrix form by adopting an image coding method, and carrying out correlation analysis on the whole genome of the autumn pears; extracting spatial features, and generating image data; analyzing the interaction among the multiple-effect gene identification data by adopting a graph convolutional network, obtaining gene interaction characteristics, identifying gene clusters participating in the same biological process, and generating a gene interaction network; and combining the gene interaction network with a deep convolutional neural network, analyzing the relationship between the genotype data and the phenotype data, obtaining deep feature data, identifying the gene locus of the pyrus ussuriensis, and generating a key gene locus analysis scheme. According to the method, the accuracy of genome correlation analysis is effectively improved, and important support is provided for gene research and accurate breeding of pyrus ussuriensis.

The present application relates to the technical field of bioinformatics and oncology, and particularly relates to a tumor driver gene identification system fusing multi-omics data and graph neural network, which comprises a data preprocessing module, a heterogeneity feature fusion module, a dynamic gene graph construction module, an improved graph neural network identification module, and a result verification and optimization module. Through standardizing the multi-omics raw data, the heterogeneity features are weighted and fused by using the attention mechanism, the dynamic gene co-expression network is constructed by combining the tumor development time series data and the clinical information, the driver gene identification is performed by using the graph attention network with embedded residual connection and multi-scale feature extraction, and the closed-loop optimization is realized by using the wet experiment data and the clinical sample feedback. The deep fusion and dynamic modeling of multi-dimensional data are realized, the accuracy, specificity and generalization ability of the driver gene identification are significantly improved, and the present application can be widely applied to the early diagnosis of tumors, the research and development of targeted drugs, and the formulation of individualized treatment plans.

The application discloses a method for screening a triple-negative breast cancer prognosis gene marker, uses gene expression data of triple-negative breast cancer, obtains genes interacting with a cell proliferation marker, and screens prognosis-related genes by means of single-factor Cox regression, and the application uses a Cox regression method with minimum maximum concave penalty to construct a prognosis risk assessment model, and obtains a prognosis gene marker and a prognosis assessment method capable of predicting and assessing the risk of triple-negative breast cancer. The application proposes a prognosis marker gene identification technology for triple-negative breast cancer which is a high-heterogeneity disease, and the technology can be applied and popularized in clinical treatment.

The invention discloses a pathogenic gene prediction method and device based on phenotypic fingerprints, equipment and a medium. The method is executed by a computer, systematic integration and quantification are carried out on associated information between genes and phenotypes of multiple dimensions, phenotype fingerprints with specific genes are constructed on the group level, and complex effects of the genes on different phenotype dimensions can be captured more comprehensively; a multi-phenotype score value taking genes as the center is calculated through gene phenotype fingerprints, that is, multi-dimensional clinical phenotype information of a target object is converted into quantitative scores taking the genes as the center on the object level, and two types of key output of pathogenic variation carrying risk assessment and candidate gene priority ranking are achieved through observation phenotypes of the target object; and the integrating degree of each candidate gene and the actual phenotype of the target object can be objectively and efficiently evaluated. According to the method, phenotype fingerprints are introduced, a multi-phenotype scoring mechanism is combined, the efficiency and objectivity of complex disease pathogenic gene recognition are remarkably improved, and the method has important clinical application value.

This invention discloses a molecular marker and its application that breaks the linkage between smooth, hairless male sterility and co-segregation of watermelon fertility genes, aiming to solve the technical problem of the current lack of methods for identifying and characterizing major watermelon fertility genes. This application develops a molecular marker that breaks the linkage between smooth, hairless male sterility and co-segregation of watermelon fertility genes (based on the watermelon 97103 reference genome V2 version, which contains a genomic region related to male sterility in the 27.94-27.95 Mb region on chromosome 8; watermelons with deletions or partial deletions of this genomic region exhibit male sterility), and designs the corresponding primer pair BY2-3. Applying this molecular marker for marker-assisted selection breeding enables faster and more accurate targeted genetic improvement of watermelon fertility, thereby shortening the breeding cycle.

Embodiments of the present application relate to a pathogenic gene identification method, device, equipment and storage medium. The method comprises: performing phenotype feature matching on a target to-be-diagnosed case and a candidate reference case to obtain a phenotype matching result; obtaining a target reference case of the target to-be-diagnosed case from the candidate reference case according to the phenotype matching result; and generating a pathogenic gene identification result of the target to-be-diagnosed case according to a case pathogenic gene of the target reference case. The technical solution of the embodiments of the present application can improve the information utilization rate and the robustness to atypical phenotypes in rare disease pathogenic gene identification, and realize efficient and accurate pathogenic gene identification.

The application provides a tissue-specific expression gene identification method and system based on three-dimensional space transcriptome, and belongs to the field of gene identification. The method comprises the following steps: obtaining three-dimensional space transcriptome data of a target biological individual at multiple continuous development stages, wherein the three-dimensional space transcriptome data comprises cell three-dimensional coordinates, cell annotation data and gene expression information; calculating a comprehensive tissue-specific score of each gene by using at least two parameters in a spatial expression concentration, a cell composition correction specificity parameter, a cross-development phase stability parameter and a significance parameter; and identifying genes of the target biological individual based on the corresponding comprehensive tissue-specific score of each gene to obtain tissue-specific expression genes. The application can effectively improve the identification accuracy of tissue-specific genes.

This invention discloses a method for analyzing single-cell tumor microenvironment data based on gene regulatory networks, comprising the following steps: acquiring raw data values ​​of several cells, performing preprocessing and identifying characteristic genes; constructing a degree gene characterization matrix based on the obtained regulatory relationships between characteristic genes, and identifying cell subpopulations; and performing cell entropy analysis, differential degree gene identification, and gene function enrichment analysis on the cell subpopulations. This invention uses the SCILE algorithm to assess the overall stemness entropy of the cell based on the importance of each gene in the gene regulatory network of each cell. Compared to traditional expression-based assessment methods, this method avoids the impact of highly expressed, low-regulated genes on cell stemness.

The present invention relates generally to biomarkers relating to liver disease. Provided herein are gene recognition features regarding non-alcoholic fatty liver disease (NAFLD), and methods of use thereof for diagnosis, classification, and monitoring of NAFLD in a subject by using a sample of extracellular vesicles from the subject.

The application discloses a high-perturbation gene priority ranking system, which comprises a gene interaction network, a hub gene identification module, a knockout perturbation initialization module and a perturbation propagation network.

This application discloses a method, apparatus, device, and medium for predicting pathogenic genes based on phenotypic fingerprinting. This method, executed by a computer, systematically integrates and quantifies the association information between genes and multi-dimensional phenotypes, constructing gene-specific phenotypic fingerprints at the population level. This allows for a more comprehensive capture of the complex effects of genes across different phenotypic dimensions. Furthermore, it calculates gene-centered multi-phenotypic scores using gene phenotypic fingerprints, transforming the multi-dimensional clinical phenotypic information of the target subject into gene-centered quantitative scores at the object level. Utilizing the observed phenotypes of the target subject, it achieves two key outputs: assessment of the risk of carrying pathogenic variants and priority ranking of candidate genes. This enables an objective and efficient evaluation of the fit between each candidate gene and the actual phenotype of the target subject. By introducing phenotypic fingerprinting and combining it with a multi-phenotypic scoring mechanism, this application significantly improves the efficiency and objectivity of identifying pathogenic genes for complex diseases, possessing significant clinical application value.