47 results about "Gene list" patented technology

The invention provides a gene detection and phenotype verification health detection method and system, and the method comprises the following steps: collecting multi-source data of a user, locking genetic variation highly related to disease and individual specificity through gene sequencing, and building an individualized genetic risk scoring system; wherein the multi-source data comprises gene sequencing and proteomics data of a user; performing gene phenotype coupling analysis, integrating multi-dimensional data of genes, proteins or phenotypes of the user, and constructing a health assessment system with high confidence; the intervention decision generation is used for generating a decision to intervene the user; through gene-phenotype coupling analysis, the disease risk prediction specificity of healthy people is improved, compared with a conventional health monitoring technology, risks are found in advance, the health behavior compliance rate of a user is increased, seamless connection with mainstream wearable equipment and an electronic medical record system is supported, and the utilization rate of data is increased.

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 present disclosure provides a method for assessing a lupus state of a patient, the method comprising: analyzing a data set comprising and / or derived from gene expression measurements of at least 2 genes selected from genes listed in Tables: 1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; and 32, to classify the lupus state of the patient, wherein the gene expression measurements are obtained from a biological sample obtained or derived from the patient.

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 construction method of immune-related key gene prognosis indexes of metastatic melanoma and related equipment, and belongs to the field of bioinformatics, the method comprises the following steps: obtaining an immune-related differential expression gene list, and constructing a weighted gene co-expression network to divide gene modules, two gene modules with the highest correlation degree with the matrix score and the immune score are screened out, and gene intersection is taken to recognize immune related key genes; and determining immune-related key genes significantly related to the lifetime, and sequentially screening through univariate Cox regression and LASSO regression to obtain genes of which the regression coefficients are not zero so as to construct and obtain immune-related key gene prognosis indexes of metastatic melanoma. The method can solve the problems that in the prior art, when a prognosis index is constructed, an immune-related gene pair is only determined by an LASSO regression coefficient, key disease treatment genes are difficult to recognize, too many immune-related genes are contained, and clinical application convenience is low.

The present application discloses a spatiotemporal omics image processing method and device based on a spatial dynamic neural network, the main purpose of which is to solve the problem that an effective tissue gene representation map cannot be generated in the existing method. It includes obtaining spatial node graph data of tissue genes and cell-to-cell communication pairs based on spatiotemporal omics images; performing convolution processing on the spatial node graph data based on the node layer in the spatial dynamic neural network that completes model training, and performing convolution processing on the cell communication pairs based on the edge layer in the spatial dynamic neural network, and the propagation between the node graph after the convolution processing of the node layer and the edge graph after the convolution processing of the edge layer is updated alternately; the output representations of the node layer and the edge layer are integrated through the attention mechanism to generate the representation map of the tissue genes.

The invention belongs to the technical field of gene scoring, and discloses a polygene scoring algorithm for risk assessment of polygene complex diseases and an application platform, and the method achieves the personalized and precise risk assessment: compared with a conventional fixed scoring model, the system combines the genes, phenotypes and environmental factors of individuals, and improves the risk assessment accuracy. And realizing individualized disease risk assessment through dynamic weighting. The dynamic weighting mechanism of the scoring model allows the model to adjust the gene weight according to individual living habits, so that the evaluation accuracy is greatly improved. And cross-group applicability: the applicability of the model on different groups is optimized by adopting a transfer learning technology, so that the scoring system has cross-region and cross-racial adaptability. The applicability of the system is enhanced, and the system is not limited to a single crowd and can be popularized globally. And dynamic data updating and feedback: allowing the user to re-score after updating the life style and health data by the system, and providing real-time and dynamic health assessment for the user.

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.

One aspect of the invention provides methods for classifying the quality of a repair response after injury to a joint of a subject. In one embodiment, the method includes providing a tissue sample from the injured region of the joint and determining expression levels in the sample of a plurality of genes, including at least those genes listed in Table 1, Table 2 and Table 3. Another aspect of the invention provides methods of treating a human or veterinary subject having an injury to a joint based on the classification of the quality of the repair response.

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 present invention discloses a method, system, device and storage medium for mapping cell subpopulation labels. The present invention applies self-supervised learning capabilities on large-scale unlabeled single-cell sequencing data sets, improves the versatility of the model, and can effectively overcome the batch effect in single-cell sequencing data. The present invention discretizes and encodes gene expression to reduce the impact of gene expression variation under different experimental conditions. The present invention strengthens the importance of transcription factors to ensure that the role of these key genes in distinguishing cells is fully considered. The present invention not only captures the information of a single gene, but also reflects the interaction between genes by combining gene representation embedding and gene expression embedding. At the same time, through a self-supervised learning strategy based on Linformer, the model can learn the basic patterns of gene expression and their interactions from a large amount of single-cell data, thereby effectively constructing a single-cell gene panorama.

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.