40 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 provides a multi-element machine learning model-based cross-species lung disease characteristic gene screening method and system, an electronic system and a storage device. The method comprises the following steps of: acquiring single cell / transcriptome data related to mouse lung diseases from a public database and preprocessing the single cell / transcriptome data; training the model by adopting six machine learning algorithms and outputting a gene importance score; calculating the weight according to the model performance and normalizing the score; and integrating the cross-species scores through a weighted fusion formula, and outputting a feature gene list and a visual report. The system comprises a data acquisition and preprocessing module, a multi-element machine learning model training module, a weight calculation and normalization module, a cross-species comprehensive scoring module and a result output module. The screening accuracy, stability and generalization ability are improved through multi-algorithm integration and cross-species fusion, and the method can be widely applied to the fields of mechanism research of lung diseases, diagnosis marker development and drug target verification.

The invention discloses a genome selection method and system based on natural language processing. According to the method, genes arranged according to chromosomes in a genome of a sample are expressed as embedded vectors by utilizing natural language processing, and the embedded vectors are input into a deep learning model, so that the phenotype prediction performance of the sample selected by the genome is remarkably improved by adopting a deep learning framework combining a convolutional neural network, a bidirectional long-short term memory network and a self-attention mechanism.

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 present invention discloses an automatic gene function prediction system based on graph neural networks and contrastive learning, comprising: a data import module for loading multiple biological network data and corresponding protein sequences and preprocessing the data; a data enhancement module for enhancing each biological network data using graph perturbations; a gene representation training module for obtaining corresponding gene representations using graph neural networks and contrastive learning; and a gene function prediction module for using support vector machines to predict whether a gene has certain specific gene functions. The present invention uses graph neural networks rather than traditional deep learning networks to fully extract information from biological network data, while using contrastive learning to capture data distribution and generate semantically rich representations, effectively improving the effectiveness of subsequent gene function prediction.

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.

This invention discloses an artificial intelligence-based method for predicting crop genotypes using images throughout their growth cycle. The method involves crop image acquisition and processing, constructing a multi-layer perceptron neural network and training a conditionally controlled generative adversarial network to learn the mapping relationship between crop genotypes, phenotypes, and images throughout their growth cycle. This method then generates a crop gene-phenotype prediction model and a phenotype-image prediction model. Finally, these two models predict, based on the predicted crop genotype, a visualization of the crop's entire growth cycle under the control of the gene in an ideal environment. This method, based on artificial intelligence technology, constructs a multi-dimensional multi-layer perceptron network and a conditionally controlled generative adversarial network. This method leverages the mapping patterns inherent in crop gene, phenotype, and image data to achieve realistic and accurate visual prediction of crops.

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 invention relates to a gene expression based biomarker that is predictive of patient clinical need for treatment that includes a PD-1 antagonist, wherein the gene expression based biomarker comprises five or more genes selected from the genes listed in Table 1 or Table 2 disclosed herein. More specifically, a negative level of a gene expression based biomarker wherein the biomarker comprises five or more genes selected from the genes listed in Table 1 or a positive level of a gene expression based biomarker wherein the biomarker comprises 5 or more genes selected from the genes listed in Table 2 is associated with favorable prognosis in a patient with cancer. Also provided are methods of treating a cancer patient with a PD-1 antagonist that were identified as positive for a gene expression based biomarker of the invention. The disclosure also provides methods and kits for testing tumor samples for the biomarkers.

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 a prediction method of spatial transcriptomics, a prediction system thereof and a storage medium. The prediction method of spatial transcriptomics comprises the following steps: determining a shared gene of spatial transcriptomics data and single cell data; constructing a first expression matrix corresponding to the shared gene in the spatial transcriptomics data, constructing a second expression matrix corresponding to the shared gene in the single cell data, and constructing a third expression matrix corresponding to the unique gene in the single cell data; constructing a similar matrix between the cells based on the spatial information; and inputting the matrix into a target function, carrying out iterative optimization by utilizing the target function, and integrating the unique gene expression of the second single cell data and the cell low-dimensional expression of the second space transcriptomics to predict the undetected gene expression. According to the technical scheme, undetected genes can be effectively predicted, molecular mechanism analysis of a biological sample is more complete, and deviation in disease research is reduced.

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.

Methods and systems for diagnosis and treatment of lupus in a patient is disclosed. The method can include analyzing a data set comprising or derived from gene expression measurements of at least 2 genes selected from the genes listed in each of one or more Tables selected from Tables: 1 to 11 to determine a set of genes enriched in a biological sample obtained or derived from the patient, and diagnosing lupus in the patient based on enrichment of the set of genes, wherein the gene expression measurements are obtained from the biological sample.

A method and device for predicting gene interaction relationships based on a graph neural network, the method comprising: preprocessing the transcriptional expression data of each experimental group, including standardizing the gene ID, and representing the gene transcriptional expression data of different experimental groups with a unified gene ID; obtaining raw data for subsequent analysis; calculating gene similarity according to each sample group, and obtaining similarity data between genes in the sample group; selecting gene pairs that meet set conditions as the basis for constructing a graph data structure by statistically analyzing and screening the data of all sample groups; further processing the raw data to ensure that all sample groups contain only genes defined in the graph structure; then, standardizing the data of these sample groups so that the data dimensions of all sample groups are consistent; and performing model training on the generated graph data group to ultimately obtain edge weight data in the graph structure, which can accurately reflect the interaction relationship between genes. The present invention can be used to analyze gene expression data, predict the interaction relationship between genes, and perform unsupervised learning in the absence of labeled data.

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.

The invention discloses a method for excavating glutinous rice wine brewing related genes. The method comprises the following steps: firstly, collecting multiple rice germplasm resources containing indica rice, japonica rice and glutinous rice, detecting SNP / CNV variation through whole genome re-sequencing, and obtaining granularity and roundness phenotype data; secondly, performing gene-phenotype association analysis by utilizing a mixed linear model MLM and combining GEMMA software, correcting a population structure and a genetic relationship, and screening out significant associated genes; finally, the candidate genes are knocked out through a CRISPR-Cas9 technology, and phenotype verification is carried out. The method disclosed by the invention brings remarkable technical progress in the aspects of capturing a complicated incidence relation between phenotypic characteristics and genomes, enriching genome data processing and MLM model training, improving the breeding efficiency of glutinous rice crops and the like, and averagely shortens a function research period from 6-8 months of traditional transgenosis to 3-4 months; and an efficient and accurate technical means is provided for improving the quality of the glutinous rice for wine brewing.