29 results about "Gene list" patented technology

The invention belongs to the technical field of essential gene prediction, and particularly relates to an essential gene prediction method based on DNA large model and time-frequency domain deep learning fusion, and the method comprises the steps: taking a domain DNA large model as a core representation layer, and obtaining special gene representation through cross-species corpus pre-training and task fine tuning; a T-Block and F-Block dual-channel time-frequency fusion structure is adopted, and the local dependence and long-range regulation relation of a gene sequence is synchronously captured by expanding DFT (Discrete Fourier Transform), complex value attention and iDFT (Initial Discrete Fourier Transform) conversion; designing an efficient modeling reasoning scheme of sliding window slices and gene-level aggregation aiming at an ultra-long sequence; in combination with class imbalance and a noise robust training strategy, cross-cell line / cross-platform transferable threshold output is realized through temperature scaling calibration, an uncertainty quantization and structured interface is matched, and drug target screening and experimental design decision are supported. The system supports the realization of multiple programming languages, and can complete low-delay end-to-end reasoning in a conventional hardware environment.

The invention discloses a method and a system for assisting in inputting clinical information of rare diseases and analyzing gene mutation based on phenotypes. The method comprises the following steps: firstly, acquiring clinical information in voice, text and image forms of a patient through a multi-source data acquisition module, converting the clinical information into characters, and performing entity recognition and standardization processing to generate structured medical record data; secondly, extracting clinical phenotypes from the structured data; furthermore, a candidate gene list is obtained according to the gene-disease relationship, comprehensive scoring and sorting are carried out, and a concerned gene list is output. According to the method, efficient structured input and standardization of clinical information are realized, the accuracy and automation level of phenotype-gene matching are remarkably improved, the gene variation interpretation period is effectively shortened, and intelligent support is provided for precise diagnosis of genetic diseases.

The invention provides a Web-based single-cell RNA sequencing data intelligent analysis system and method. The method comprises the following steps: receiving a cell group through a Web interface; performing differential gene analysis on the single-cell RNA sequencing data contained in the cell group to obtain an original differential gene list, and filtering the original differential gene list by adopting a multi-threshold screening algorithm to obtain a target differential gene list; performing species automatic identification processing on the target differential gene list, calling a target local gene set database based on a result of the species automatic identification processing, and performing parallel enrichment analysis independent of network connection on the target differential gene list according to the target local gene set database to obtain a gene enrichment analysis result; and generating an interactive chart by adopting an intelligent label anti-overlapping algorithm so as to visualize the interactive chart. According to the method, the problems of high operation threshold, low batch analysis efficiency, unstable result and poor interactivity in the prior art are solved.

The invention provides a feature gene selection method and system based on deep learning attribution analysis and beam combination optimization, and relates to the technical field of signal analysis, and the method comprises the following steps: obtaining single cell RNA sequencing data and batch RNA sequencing data; the method comprises the following steps: based on single-cell RNA sequencing data, obtaining an initial gene pool aiming at a plurality of disease subtypes through a consensus screening strategy fusing XGBoost multi-dimensional importance measurement and deep learning SHAP attribution analysis; performing first-stage optimization on the gene list of each subtype by taking the initial gene pool as a starting point and adopting beam combination search and based on a global discrimination objective function to obtain a first-stage optimized gene set; integrating the first-stage optimized gene set with the differential expression gene set of the batch RNA sequencing data, and carrying out second-stage optimization by adopting bundle combination search again to obtain a second-stage optimized gene set; and performing cross validation integration on the second-stage optimized gene set, and outputting a final feature gene set.

The application provides a kind of genotypes and phenotypic association analysis method based on multiple phenotypes, belongs to the technical field of bioinformatics, and establishes SNP-gene-phenotype three-layer network to explore the relationship between genotypes and phenotypes using statistical data, and the internal correlation between genomics and genomics, such as genes and genes, and phenotypes and phenotypes, is considered in the three-layer network construction process, which is more consistent with biological reality;Solve the problem that clinical data is difficult to obtain and SNP and phenotype correlation cannot be predicted;The biological pathway correlation between SNP-gene-phenotype different omics layers is analyzed.Through the analysis of various database data, it is found that the internal correlation of each database omics has quantitative value, while the correlation between omics generally has qualitative value, i.e. the correlation is 1 and the non-correlation is 0.Through the establishment of three-layer model, the key correlation genes can be predicted using the quantitative value of the internal omics and the qualitative value of the inter-omeric relationship, and the pathway relationship score between each layer is calculated to analyze the pathway relationship between SNP-gene-phenotype.

The invention belongs to the field of plant molecular genetics and crop breeding, and provides a rapid identification method and a creation method of an extremely early rice variety. According to the invention, a specific genotype combination causing the rice to present an extremely early-maturing phenotype is clear and verified for the first time, i.e., the three genes Ghd7, Ghd7.1 and Ghd8 are represented as function deletion alleles, and meanwhile, the Hd1 gene is represented as a functional alleles. Based on the specific combination, genotype identification is performed on a plurality of known extremely early rice varieties in northeast and southern China, and results are completely identical. Furthermore, a novel rice material with the genotype combination is created through a gene editing technology, and a typical strain with an extremely early heading stage is successfully obtained, so that the accuracy and reliability of the combination are verified on the molecular level. According to the invention, the problem of inaccurate prediction caused by gene interaction complexity in the prior art is solved, and a powerful molecular tool is provided for early-maturing breeding of rice.

The present disclosure provides a method for performing phenotypic fit analysis. The method comprises computer processing an input dataset to produce a set of genes and a set of gene-phenotype associations (GPAs) associated with the set of genes. The method further comprises determining, for a subject, a plurality of subject-gene similarity subscores, based at least in part on the GPAs associated with the set of genes. The method further comprises determining a predicted likelihood of association between the subject and at least a subset of the set of genes, based at least in part on the plurality of subject-gene similarity subscores.

The application discloses a method for constructing a gene regulation network based on meta-analysis, and aims to solve the problem that a gene regulation network has a large error and a low accuracy because different research data are not completely homogeneous when the gene regulation network is expanded by combining data of multiple studies to enlarge a sample size, and the method comprises the following steps: performing meta-analysis on each transcriptomic gene expression dataset obtained to obtain a significant differential gene list; obtaining a transcription factor gene list according to a species to which the transcriptomic gene expression data belong, and generating a core gene list according to the transcription factor gene list and the significant differential gene; calculating a Pearson correlation coefficient of each core gene and each significant differential gene, and constructing a co-expression network according to the Pearson correlation coefficient; clustering the co-expression network to obtain a plurality of differential gene modules with high correlation of the core genes; and combining biological knowledge and a structural equation model to construct a corresponding gene regulation network according to each module. The application belongs to the field of gene regulation networks.

The application discloses a kind of gene sequence pre-training method and device of fusion knowledge graph, by considering the regulation relationship between genes to construct gene regulation graph, and increase motif and bin in gene regulation graph to construct knowledge graph based on gene regulation network, and then learn gene representation in knowledge graph, and the gene representation in knowledge graph is introduced as special token in the gene sequence of gene, improve the prediction accuracy of MLM model to mask, and obtain accurate gene representation, the initial vector of gene in the expansion gene regulation graph is learned as gene representation in sequence, gene representation is extracted again by pluggable representation model, such alternating process realizes the interaction of knowledge graph information and gene sequence information, gene representation is extracted using interactive training MLM model, which can improve and then improve the accuracy of gene correlation property prediction.

Provided is a small RNA, a composition comprising the small RNA, a method for using same and use of same. The small RNA or composition can inhibit the ability of any one or more of pathways or genes listed in Table 3, or decrease or down regulate the expression level of IL-1 beta, IL-6, and / or TNF-alpha in vitro or in vivo, or treat or prevent IL-1 beta, IL-6, and / or TNF-alpha related diseases and / or increase cell viability in a subject.

The invention discloses a cancer-related gene comprehensive scoring method and system based on multi-modal deep learning. Comprising the following steps: 1, obtaining scores corresponding to gene expression, copy number variation, methylation and somatic mutation processes, forming a feature vector by using the gene expression score, the copy number variation score, the methylation score and the somatic mutation score, and preprocessing the feature vector to obtain a standardized feature vector; 2, carrying out weight fusion on the standardized feature vectors, and calculating a comprehensive score; 3, constructing a multi-modal integrated model, and training the model by taking the comprehensive score as a model training target; and 4, carrying out score prediction on the genes by utilizing the trained multi-modal integrated model, outputting predicted scores, and carrying out gene list sorting according to the predicted scores. The method is suitable for mining key genes related to cancer occurrence and development from high-throughput sequencing data, and can be applied to tumor molecular mechanism research, prognostic marker screening and precise medical scheme formulation.

The present application belongs to the technical field of biological breeding, and relates to a chicken feather color related molecular marker combination, primers, and a method and application for determining feather color, breeding and / or producing specific feather color chickens. The present application provides a chicken feather color related molecular marker combination, which comprises an MC1R gene, a PMEL17 gene, an SLC45A2 gene, a TYR gene and a SOX10 gene. The present application solves the problem that the existing single marker molecular breeding is not widely applicable, and according to the combination mode of various genes listed in the present application, a target feather color chicken complete set can be quickly and effectively bred.

The application provides a kind of screening method, system, electronic system and storage device of cross-species lung disease characteristic gene based on multi-element machine learning model, which comprises: obtaining human and mouse lung disease related single cell / transcriptome data from public database and preprocessing;Six machine learning algorithms are used to train the model and output gene importance score;According to the performance of the model, the weight is calculated and the score is normalized;The cross-species score is integrated by the weighted fusion formula, and the characteristic gene list and the visualization report are output.The system includes data acquisition and preprocessing module, multi-element machine learning model training module, weight calculation and normalization module, cross-species comprehensive scoring module and result output module.The screening accuracy, stability and generalization ability are improved by multi-algorithm integration and cross-species fusion, which can be widely used in the mechanism research, diagnostic marker development and drug target verification field of lung disease.

The invention relates to a strong promoter D8 suitable for streptomyces and application thereof, and relates to the field of genetic engineering and microbial metabolism engineering. The nucleotide sequence of the strong promoter D8 is as shown in SEQ ID No.1, and the strong promoter D8 comprises a plasmid vector of the strong promoter; the invention also discloses a host cell containing the plasmid vector, and application of the strong promoter, the plasmid vector and the host cell in starting expression of a target gene. Compared with the prior art, characterization of the promoter D8 provides a more effective tool element for streptomyces promoter engineering and high-efficiency gene expression, and the promoter D8 has important significance on streptomyces silent gene characterization, high-efficiency gene expression, metabolite synthesis, metabolic pathway reconstruction and the like. The strong promoter can be applied to common streptomyces type strains, and has important significance on high yield of important proteins including enzymes and important metabolites from actinomycetes.

To provide a prediction method and a prediction system for knowing the presence / absence of a comorbidity in a first disease and a second disease with high accuracy even when there is no common gene.SOLUTION: A method of predicting the likelihood of co-morbidity includes the steps of (a1) calculating k p-values using a genetic list of a first disease and k functional gene sets; (a2) calculating k p-values using a genetic list of a second disease and the k functional gene sets; (a3) calculating a first semantic profile using the k p-values calculated in (a1); (a4) calculating a second semantic profile using the k p-values calculated in (a2); and (a5) calculating a Pearson Correlation Coefficient using the first semantic profile and the second semantic profile.SELECTED DRAWING: None

The invention discloses an interpretable crop genome prediction deep learning model, puts forward a deep learning framework Cropform, fuses a convolutional neural network (CNN) and a multi-head self-attention mechanism, constructs a technical scheme integrating phenotype prediction and gene mining, automatically extracts local features of genome data through the CNN, and provides an explainable crop genome prediction deep learning model. In combination with a multi-head self-attention mechanism, global association among features is captured to realize high-precision prediction of complex phenotypes, and the prediction accuracy is maximally improved by 7.5% compared with CropGBM, DEM and the like. Key SNPs and genes can be accurately positioned through attention weight and SHAP value analysis, a genetic variation mechanism is disclosed, and multi-modal data fusion of SNP, InDel, gene expression and the like is supported to further improve performance. In order to improve practicability and convenience, the Cropform provides a free online network server. According to the method, the black box limitation of a traditional deep learning model is broken through, analysis of gene-phenotype association is assisted, and an efficient tool is provided for crop genome design and breeding.

The embodiment of the invention relates to the crossing field of generating drug molecules by using a large language model, in particular to a phenotypic drug molecule discovery method, device and equipment based on cell transcriptomics and a computer readable storage medium. The method comprises the following steps: acquiring transcriptome sequencing result data to be analyzed, and extracting a differential expression gene list from the transcriptome sequencing result data; obtaining a differential gene list text description corresponding to the differential expression gene list; inputting the differential gene list text description into a trained molecular generation model to obtain at least one candidate molecular structure representation; the molecular generation model is used for converting the differential gene text description serving as gene expression information into a molecular reasoning result causing the change. The method can break through the dependence of traditional drug research and development on a known compound library, brand new molecules meeting functional requirements are generated, and the chemical space of drug discovery is expanded.

A method for assessing a lupus nephritis disease state of a patient, the method comprising: analyzing a data set comprising or derived from gene expression measurement data of at least 2 genes or human orthologs thereof selected from the genes listed in Tables 19-1 to 19-36, Tables 19A-1 to 19A-36, Table 20, Table 21, Table 22, Tables 23-1 to 23-28, Tables 25-1 to 25-32, Tables 26-1 to 26-60, Tables 27-1 to 27-48, and Tables 28-1 to 28-22 in a biological sample from the patient, to classify the lupus nephritis disease state of the patient.

The invention relates to a method for monitoring the treatment of a subject undergoing therapy with an active that is naltrexone or a metabolite or analogue thereof, comprising:measuring the gene expression profile of any of the genes listed in Table 1 or Table 2, in a sample of CD3+ cells obtained from the subject undergoing treatment;wherein if the expression of any of the genes in Table 1 is increased compared to a control, or if any of the genes listed in Table 2 is decreased compared to a control the active is being administered at an effective level.

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.

According to the present invention, a system: acquires a dataset composed of gene expression data of a sample belonging to either of two groups; generates a gene network representing interactions between a plurality of genes by referring to a pathway database; selects one gene from among the plurality of genes; extracts a path by following the gene network downstream with the selected gene as a starting point; calculates path scores by executing enrichment analysis by using, as inputs, a list of genes included in the path and the dataset; generates a ranked gene list by ranking the plurality of genes on the basis of the path scores of the plurality of genes; and calculates drug scores by executing the enrichment analysis by using, as inputs, a list of target genes of drugs stored in a drug database and the ranked gene list.

Target genes are identified with high accuracy and the effectiveness of drugs is evaluated. [Solution] The system acquires a dataset consisting of gene expression data of samples belonging to one of two groups, references a pathway database to generate a gene network representing the interactions of multiple genes, selects one gene from the multiple genes, traces the gene network downstream starting from the selected gene to extract a path, calculates a path score by performing enrichment analysis using as input a list of genes included in the path and the dataset, ranks the multiple genes based on the path scores of the multiple genes to generate a ranked gene list, and calculates a drug score by performing enrichment analysis using as input a list of genes that are targets of drugs stored in a drug database and the ranked gene list.

The present disclosure provides a method for performing phenotypic fit analysis. The method comprises computer processing an input dataset to produce a set of genes and a set of gene-phenotype associations (GPAs) associated with the set of genes. The method further comprises determining, for a subject, a plurality of subject-gene similarity subscores, based at least in part on the GPAs associated with the set of genes. The method further comprises determining a predicted likelihood of association between the subject and at least a subset of the set of genes, based at least in part on the plurality of subject-gene similarity subscores.

The invention relates to the technical field of electric data processing, in particular to a maintenance task scheduling method and system.The maintenance task scheduling method comprises the steps that chromosomes composed of a to-be-scheduled maintenance task set are obtained, and all genes in the chromosomes represent scheduling schemes corresponding to the genes; and for any chromosome, calculating a mean value of initial fitness functions of all genes in the chromosome as a final fitness function of the chromosome, and carrying out iteration through screening, crossing, variation and population updating in a genetic algorithm to achieve a set algebra or fitness convergence so as to obtain an optimal chromosome. According to the method, individuals capable of performing crossover and mutation operation are screened according to the final fitness function of the corresponding chromosome, so that generation of illegal offspring chromosomes is reduced fundamentally, the search efficiency of a genetic algorithm under the background of maintenance task scheduling is improved, and the quality of a final output scheduling scheme is improved.

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.

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.

The application provides a Web-based intelligent analysis system and method for single-cell RNA sequencing data, which comprises the following steps: receiving a cell group through a Web interface; performing differential gene analysis on single-cell RNA sequencing data contained in the cell group to obtain an original differential gene list, filtering the original differential gene list by using a multi-threshold screening algorithm to obtain a target differential gene list; performing automatic species identification processing on the target differential gene list, calling a target local gene set database based on the result of the automatic species identification processing, performing parallel enrichment analysis on the target differential gene list without network connection according to the target local gene set database, and obtaining a gene enrichment analysis result; and generating an interactive chart by using an intelligent label anti-overlapping algorithm to visualize the interactive chart. The application solves the problems of high operation threshold, low batch analysis efficiency, unstable result and poor interactivity in the prior art.

The application discloses a gene-phenotype correlation analysis model and a method and application thereof, and belongs to the technical field of biological medicine. The model establishment method comprises the following steps: S1, collecting known trait-gene data to form a gene-trait pair; S2, calculating the rare mutation type score of each gene by using a formula; S3, analyzing the correlation between the mutation score and the trait by linear regression, calculating the weight of each mutation type, and optimizing the weight combination, so that the correlation R 2 is taken as the evaluation standard; S4, calculating the rare mutation load score of a sample gene according to the scoring formula and the optimized weight; S5, analyzing the correlation between the mutation load score and the phenotype by a regression method, and constructing a gene-phenotype correlation analysis model. Compared with a traditional gene-base collapsing method, the model has good reproducibility and complementarity, and can be used for discovering candidate risk genes of new traits or unknown diseases.