165 results about "Gene sets" patented technology

The invention relates to the technical field of bioinformatics, and provides a crop whole genome phenotype prediction method and system fused with an environmental indicator gene, and the method comprises the following steps: collecting re-sequencing data, and carrying out genome comparison to obtain variation site data; performing whole genome association analysis by using the variation site data to obtain phenotype association site information; carrying out gene expression quantity measurement on samples of the crop population material in different environments to obtain gene expression quantity data; performing differential expression analysis on the gene expression quantity data to screen environmental indicator genes to obtain an environmental indicator gene set; constructing a phenotype prediction model of double-branch fusion; and predicting a to-be-predicted material through the phenotype prediction model to obtain phenotype prediction results for different environments. According to the method, environmental factors are incorporated into the whole genome selection model, so that the phenotype prediction precision in different environments is improved.

The invention provides a construction method of a predictive cell senescence model, a method for predicting the senescence state of a tissue sample based on the senescence model, a method for screening potential therapeutic drugs, equipment, a medium and a program product, and relates to the field of intelligent medical treatment. The model construction method comprises the following steps: acquiring a training set sample expression profile data set; identifying a key senescence gene set from the data set by using a feature selection algorithm; inputting the key senescence gene set into a machine learning model to fit a prediction model, and determining an optimal hyper-parameter to obtain a cell senescence model containing the weight of a single gene in the key senescence gene set; the cell senescence model is a senescence score obtained by calculating the sum of the product of the expression quantity of a single gene and the regression coefficient thereof. The cell senescence model, namely PreCSenM, is constructed by integrating a plurality of senescence characteristic gene sets and a gene scoring algorithm, the accuracy in CS evaluation is superior to that of 10 existing methods, and the application of CS from biological research to clinical scenes is also realized.

The invention provides an EGFR wild-type lung adenocarcinoma prognosis risk assessment method based on multi-omics and machine learning, and the method comprises the steps: obtaining multi-omics and clinical data of lung adenocarcinoma, obtaining a data set, and carrying out the multi-omics consensus clustering, and obtaining a molecular typing result; high-risk subtype specific candidate genes are identified, a candidate prognosis gene set is obtained, multi-algorithm machine learning comparison optimization is carried out, and a modeling strategy is obtained; performing feature screening and model training to obtain a multi-omics feature model so as to calculate an individual risk score of the to-be-tested sample; the individual risk score and the clinical staging information are utilized to obtain a clinical column diagram and a survival prediction result, then the flow of the multi-omics feature model, the individual risk score and the survival result is Web to obtain a clinical system, and a lung adenocarcinoma prognosis risk assessment result is output. The invention can realize an objective, accurate, generalizable and multifunctional prognosis evaluation and treatment guidance tool, and has important clinical application value and wide industrialization prospect.

The invention relates to a training method of a respiratory tract infection disease progress and prognosis prediction model. The training method comprises the following steps: extracting mRNA from peripheral blood of a target patient, and carrying out transcriptome sequencing to obtain a sequencing result; based on the ferroptosis related gene set, comparing ferroptosis score differences of two groups of patients with community-acquired pneumonia and sepsis, and screening corresponding ferroptosis related genes with statistical significance from a sequencing result; screening out genes meeting preset conditions from the ferroptosis related genes based on LASSO regression; and establishing an RTI clinical outcome prediction model through logistic regression by taking whether the patient is sepsis or not as an outcome dichotomy variable and taking the screened gene expression quantity as a prediction variable. According to the invention, after the prediction model is subjected to machine learning screening such as LASSO and the like, the core feature with the highest prediction value is reserved, so that the risk of over-fitting of the model on training data is reduced.

The invention discloses a method, a device and equipment for identifying a communication relationship between cells, and a storage medium, and relates to the technical field of bioinformatics, direct causal contribution between the cells is quantified by using a causal association relationship of a source cell gene to a target cell gene, the communication relationship between the cells can have explanatory force at a causal level, and the identification accuracy of the communication relationship between the cells is improved. Further, the overall regulation effect among the cell populations is accurately evaluated. The method comprises the following steps: acquiring gene expression data of different cell types, wherein the different cell types at least comprise source cells and target cells; aiming at the candidate genes of the target cell, constructing different hypothesis models by using the covariable gene set, and determining a causal association relationship between the source cell gene and the target cell gene through the different hypothesis models; and calculating the communication intensity of the source cell to the target cell according to the causal association relationship of the source cell gene to the target cell gene, so as to identify the causal regulation relationship between the cells through the communication intensity.

The invention relates to the technical field of medical biology, in particular to a chronic pancreatitis complication risk grading system based on exosome transcriptome data, and aims to solve the problems that existing chronic pancreatitis (CP) typing lacks molecular basis, complication risk prediction is weak and clinical transformation is poor. According to the system, plasma exosomes are extracted and sequenced, a functional characteristic gene set is constructed in combination with pancreas single cell data, CP is divided into three risk increasing subtypes by using a COCA algorithm, and finally 12 core miRNAs are screened to construct a BPNN diagnosis model. The invention proves that the plasma exosome can be used for staging classification of chronic pancreatitis for the first time, miRNA non-invasive accurate layering illness conditions can be detected through qPCR, the risk of fatty diarrhea and 3c type diabetes mellitus can be predicted, and the non-invasiveness, convenience, classification accuracy and result repeatability of the plasma exosome have clinical application and transformation advantages.

Provided herein are compositions and methods for identifying the cell type origin of cells based on RNA sequencing (RNA-seq) data.The method provided herein may include: receiving a plurality of sequencing read counts; providing a set of detected cell types and a related gene set G for each cell type, such that each gene set G comprises a plurality of genes g; scoring the sequencing read counts to generate an assignment score; and assigning each cell among the plurality of cells to the cell type with the highest assignment score, thereby identifying the cell type origin of each cell.This method can be implemented, for example, using a computer system.

The invention belongs to the technical field of biomedicine, and particularly relates to a renal clear cell carcinoma prognosis model construction method and system based on a metabolic gene set. The system comprises: a metabolic gene set data acquisition module for acquiring metabolic gene set data of a patient with renal clear cell carcinoma, the metabolic gene set data including CYP3A7, ST3GAL5, DBH, UGT2B7, GCNT4, LIPA, ITPKB, HS3ST1, GYG2, and CYP51A1; the survival time calculation module is used for calculating the survival time of the renal clear cell carcinoma patient based on the prediction model; the prediction model comprises a formula I for calculating the risk score of the patient and a formula II for calculating the survival probability of the patient at a certain time point; and the result output module is used for outputting a prognosis result of the patient based on the survival time of the renal clear cell carcinoma patient. The system can significantly improve the accuracy and reliability of prognosis of ccRCC patients, and has wide application prospects and commercial values.

The invention belongs to the technical field of biomolecular markers, and particularly relates to a cross-animal general skeleton-derived hematopoietic stem cell marker gene set and a screening method thereof. According to the invention, a bone-derived hematopoietic stem cell marker gene set universal across animal categories is constructed for the first time, and the bone-derived hematopoietic stem cell marker gene set comprises at least five of Cdc42, Cbx, Tfam, Denr, Mcts1, Ak2, Ruvbl, Ahcy, Nna and Vdac; the hematopoietic stem cell marker gene set is obtained through cross-species homologous gene screening, the defect that a traditional vertebrate marker has no orthohomology in invertebrates is overcome, accurate recognition of HSC in shells is achieved, the hematopoietic stem cell marker gene set has species universality and cell specificity, the immune state of aquatic animals can be evaluated, disease-resistant breeding can be guided, and the application prospect is wide. And molecular evidence is provided for analyzing an evolution path of a hematopoietic system from invertebrates to vertebrates.

The invention provides a country IP incubation method and device and an IP incubation system, and the method comprises the steps: obtaining the cultural data of a country, wherein the cultural data comprises the text cultural data, the visual cultural data and the audio cultural data; feature vectors of the culture data are extracted and fused, and a multi-modal culture feature matrix is generated; performing culture gene analysis on the multi-modal culture feature matrix to obtain a gene set which contains symbol vectors, mental prototype vectors and corresponding culture gene entropy values; and creating a rural IP incubation scheme according to the gene set. According to the technical scheme, the technical problems that in the prior art, data integration is low in efficiency, core element screening subjectivity is high, and market verification is lagged in rural culture IP development can be effectively solved.

The application discloses a gene regulation network inference method combining information theory and machine learning, including obtaining time series of different gene expression processes, converting the time series into symbol sequences, calculating the symbol transition entropy between different gene symbol sequences, calculating the regulation gene set of each gene, constructing a model for the time series of a target gene and the time series set corresponding to the regulation gene set of the target gene and training the model, calculating the importance score of the regulation gene, screening the regulation gene with the importance score meeting a first threshold value and adding the regulation gene into a core regulation gene set; obtaining the symbol transition entropy of all core regulation genes to the target gene, combining the importance score and the symbol transition entropy of the core regulation gene into a regulation coefficient after normalization, screening the core regulation gene set meeting a second threshold value, and obtaining the core regulation gene set of all target genes. The method reduces the calculation complexity, solves the overfitting problem of the model based on machine learning, and improves the prediction accuracy.

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 relates to the technical field of biomedical detection and bioinformatics, and discloses a detection method and system for social anxiety disorder risk assessment, and the method comprises the steps: obtaining transcriptome data of a peripheral blood sample of a to-be-detected object, and carrying out preprocessing and normalization to obtain a standardized gene expression matrix; extracting minimum gene set expression data containing 10 genes such as HSF5 and FADS2, and performing Z-score standardization processing by using the solidified model parameters; calling a preset weight coefficient and an intercept item to perform linear weighting and probability conversion calculation on the standardized data to obtain a disease prediction probability of the subject; and carrying out risk layering according to the optimal critical value and generating an auxiliary diagnosis report. According to the method, stable features are screened through a machine learning algorithm, the scoring model is constructed, subjectivity of traditional clinical diagnosis is overcome, and objective, quantitative and automatic evaluation of social anxiety disorder risks is achieved.

The invention discloses a method for biosynthesizing ginsenoside by taking tobacco BY-2 cells as a chassis, and belongs to the technical field of bioengineering. The method comprises the following steps that firstly, a recombinant expression vector containing a ginsenoside synthesis pathway key gene set is constructed, and the gene set comprises genes such as tHMGR, SE and SS from ginseng and Pn1-31 and Pn3-31 genes from panax notoginseng; secondly, introducing the recombinant vector into tobacco BY-2 cells, and screening through a solid culture medium to obtain a pure positive cell line; and finally, culturing the positive cell line in a liquid or solid culture medium to obtain ginsenoside Rh2 and / or Rg3. By utilizing the advantages of fast growth and stable culture of the tobacco BY-2 cells, a rare ginsenoside synthesis pathway is successfully reconstructed, efficient heterologous synthesis of the rare ginsenoside is realized, and a repeatable technical scheme is provided for industrial production of the rare ginsenoside.

The invention discloses an identification method, device and system for a high gene module scoring area, and relates to the technical field of space transcriptomics. The identification method comprises the following steps: acquiring space transcriptome data, dividing cells into a plurality of unit grids according to space coordinate information of the cells, and determining corresponding grid space coordinates for each unit grid; calculating a module score value of each cell and determining a high-score cell set; calculating an integrated gene set scoring index; and carrying out region division on the unit grid and determining a high gene module scoring region. According to the method, a plurality of genes are analyzed as a functional whole, the limitation that traditional single-gene analysis cannot reveal collaborative regulation is overcome, spatial quantization of a gene set synergistic effect is realized, and the problem that a gene set enrichment region cannot be positioned in the prior art is solved.

The application provides a prostate cancer biochemical recurrence prognosis risk prediction model based on fatty acid metabolism and cancer cell stemness genes and a construction method thereof, wherein the construction method comprises the following steps: S1: data collection: obtaining prostate cancer sample transcriptome data with biochemical recurrence information from a database, and dividing the data into a model training set and a model test set; S2: stemness score analysis; S3: fatty acid metabolism score analysis; S4: based on the analysis results of S2 and S3, identifying a co-expression gene module related to fatty acid metabolism and stemness characteristics in prostate cancer by a co-expression similarity algorithm and a hierarchical clustering algorithm, and obtaining a fatty acid metabolism and stemness-related gene set; S5: constructing a prostate cancer BCR prognosis risk prediction model; S6: constructing a nomogram model; S7: extracting RNA of a to-be-tested sample, constructing a cDNA library, quantifying the expression of the above genes, and calculating the prognosis risk level of prostate cancer through the expression level.

The embodiment of the present application discloses a genotype classification method for diffuse large B-cell lymphoma, a computer device, and a computer-readable storage medium. The method includes the following steps: testing a sample according to a preset specific gene set to obtain variation detection data; preprocessing the variation detection data to obtain variation information; generating an initial feature matrix based on the variation information, screening the initial feature matrix to obtain a feature matrix, and the feature matrix is ​​used to characterize the gene variation contained in the specific gene set in the corresponding sample; obtaining a first genotype label and an important feature set, constructing a data set based on the feature matrix, the first genotype label, and the important feature set, and training a classification model; obtaining a second genotype label output by the classification model, and determining a genotype classification report based on the second genotype label. Therefore, the present application can reduce the cost of testing, can effectively predict the patient's genotype, and has a high clinical application value.

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 embodiment of the invention discloses a single cell transcriptome data processing method and device, a parameter library and electronic equipment, and the method comprises the steps: obtaining a common parameter, the common parameter comprises a reference feature gene set and a reference association relationship between an original feature and an extracted feature, the reference feature gene set comprises a plurality of feature genes, and the reference association relationship comprises a reference association relationship between the original feature and the extracted feature; the reference association relationship is used for dimension reduction processing of a gene expression condition; based on the reference feature gene set and the single cell transcriptome data of the to-be-detected sample, determining the gene expression condition of the feature gene in the to-be-detected sample; on the basis of the gene expression condition of the feature gene in the to-be-detected sample and the reference association relationship, performing first data dimension reduction processing to obtain a first dimension reduction result; wherein the to-be-detected sample and the common parameters belong to the same biological tissue type. By adopting the embodiment of the invention, the computing resource demand can be effectively reduced, and the data processing efficiency is improved.

The application provides a method for identifying potato drought-resistant genes using hyperspectral, and relates to the technical field of crop drought-resistant gene identification, and comprises the following steps: setting a drought group and a control group, synchronously acquiring plant canopy hyperspectral images and transcriptome data and establishing a correlation data set; extracting spectral features, screening drought response candidate genes; calculating the correlation coefficient of each candidate gene and the spectral features based on Spearman correlation analysis, and determining the primary gene set according to whether the average value of a plurality of positions after the absolute value is sorted exceeds a threshold value; constructing a random forest regression model with the spectral features as input and the expression amount of the primary gene as output, predicting the expression amount through a verification sample, and screening target drought-resistant genes according to the coefficient of determination and the prediction error; constructing a target gene overexpression plant, measuring the relative change rate of physiological indexes and the change multiple of gene expression amount under drought, calculating a comprehensive function verification index, and determining the gene as a drought-resistant gene if the index exceeds a threshold value.

The application discloses a method for biosynthesizing ginsenoside by using tobacco BY-2 cells as a chassis, and belongs to the technical field of bioengineering. The method comprises the following steps: firstly, a recombinant expression vector containing a key gene set of a ginsenoside synthesis pathway is constructed, wherein the gene set comprises tHMGR, SE, SS and other genes derived from Panax ginseng and Pn1-31 and Pn3-31 genes derived from Panax notoginseng; secondly, the recombinant vector is introduced into tobacco BY-2 cells, and a pure positive cell line is obtained through solid culture medium screening; finally, the positive cell line is cultured in liquid or solid culture medium to obtain ginsenoside Rh2 and / or Rg3. The application successfully reconfigures a rare ginsenoside synthesis pathway by taking advantage of the fast growth and stable culture of tobacco BY-2 cells, realizes efficient heterologous synthesis of the rare ginsenoside, and provides a repeatable technical scheme for industrial production of the rare ginsenoside.

The invention relates to the technical field of methylation detection site detection, and particularly provides an early gastric cancer lymph node metastasis risk prediction method, system, application and medium, and the method comprises the following steps: obtaining a gastric cancer public data set containing a DNA methylation chip data set and an RNA sequencing data set, and carrying out sample screening, quality control and grouping processing to obtain eight quality control grouping samples; carrying out methylation and RNA difference analysis on the quality control grouped samples to obtain a related gene set of differential methylation sites and differential methylation regions and an RNA differential expression gene set; a gene set of differential methylation sites and an RNA differential expression gene set are integrated and screened to obtain eight target genes. The system comprises a sample acquisition module, a gene analysis module and a gene screening module. Target genes are analyzed and screened on the basis of database biological information, and methylation detection sites for histopathological specimens are screened in combination with lymph node metastasis positive and negative early gastric cancer histological sample verification.

The invention provides a genome annotation method and an electronic device. The genome annotation method comprises the following steps: S1) performing gene structure prediction on a genome by adopting multiple modes to obtain multiple prediction gene sets; s2) performing gene integration on the multiple predictive gene sets by using an EVM tool to obtain an integrated gene set; s3) performing BUSCO evaluation on the integrated gene set to obtain an integrated gene set evaluation file; s4) performing BUSCO evaluation on the genome to obtain a genome evaluation file; and S5) correcting the integrated gene set evaluation file by using the genome evaluation file to obtain a corrected gene set, wherein the gene structure prediction comprises transcriptome prediction, de novo prediction and homologous prediction.The genome annotation method can significantly improve the accuracy and integrity of gene annotation.

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 disclosure relates to a method for assessing prostate cancer progression, aggressiveness, and therapy outcomes by employing transcriptome-based metrics. The disclosure also relates to a method of computing a Stemness score by correlating a prostate cancer sample's gene expression profile with a stem cell signature derived from a machine learning algorithm. The disclosure further relates to a method of calculating a PCa-Stem signature score by performing single-sample gene set enrichment analysis on at least twelve genes, including HMMR, AURKB, CENPA, DEPDC1B, HJURP, PBK, MELK, UBE2C, DLGAP5, NEK2, BIRC5, and KLK12. The method may also involve comparing the Stemness and PCa-Stem signature scores to clinical cohort benchmarks to determine prostate cancer stage, aggressiveness, or risk category. Prostate cancer samples characterized by high Stemness or PCa-Stem signature scores may indicate aggressive disease and correlate with poor therapy outcomes.

The invention provides a brain glioma immune microenvironment complexity assessment method and application, and the method comprises the following steps: obtaining m glioma samples, and carrying out transcriptome determination on each cell in each glioma sample to obtain transcriptional expression profile data of the cells; carrying out clustering analysis on transcriptional expression profile data of the single cells through K-means clustering, and preliminarily identifying lymphocytes and myeloid cells according to immune cell classical markers; carrying out clustering analysis on the identified transcription expression profile data of the lymphocytes and myeloid cells, and identifying n cell subgroups; calculating the enrichment score of the characteristic gene set of each cell subset in the glioma transcriptome sample, wherein the higher the score is, the higher the enrichment proportion of the cell subset in the sample is; and evaluating the complexity of immune cell enrichment in the glioma sample based on the enrichment score of the characteristic gene set. The immune cell infiltration complexity can be predicted, and the relationship between immune cell enrichment complexity and brain glioma prognosis can be identified.

The application relates to the technical field of biological information, and discloses a plant stress relief key gene tracing method and system based on transcriptome data. The method comprises the following steps: performing high-throughput transcriptome sequencing on plant leaves of four treatment groups, obtaining clean transcriptome data through quality filtering; obtaining a whole genome expression matrix through transcript assembly and redundancy removal processing, performing intersection screening on stress induction up-regulated genes and relief agent silencing down-regulated genes, and obtaining a stress induction-relief agent silencing candidate gene set; constructing a stress induction co-expression topology network by taking the candidate gene set as a silencing annotation basis, calculating a weighted neighbor silencing rate to obtain a topology feature matrix; training a selective silencing probability prediction model through a logistic regression model by taking the topology feature matrix and the candidate gene set, and performing channel enrichment calculation by taking the silencing probability as a weight to screen and obtain a core tracing gene set. The application improves the biological credibility of key gene tracing results and the cross-scene adaptability of the method.

The invention relates to a method and a kit for detecting SMA related gene copy number based on an NGS technology. The detection method comprises the following steps: extracting DNA of a sample to be detected and constructing an NGS library; designing a specific capture probe based on SMN1 / SMN2 differential sites a, b and c, and performing whole exon sequencing to obtain original sequencing data; performing data preprocessing on the sequencing original data; selecting a human control gene set as a housekeeper gene candidate set, and screening n genes close to SMN1 and SMN2 whole exon sequencing depth from the housekeeper gene candidate set as housekeeper genes k; standardizing the sequencing depth of each housekeeping gene k, and normalizing to obtain a theta value; the average value of all housekeeping genes theta is recorded as a correction factor value theta, and the copy states of the SMN1 and SMN2 genes in the DNA sample to be detected are corrected. The method has the beneficial effects that the quantitative accuracy of the copy number of the SMN1 and SMN2 genes is improved, and meanwhile, the detection efficiency is improved.

The invention provides a bladder cancer subtype classification system and molecular typing method based on machine learning, and the molecular typing method comprises the steps: firstly obtaining transcriptome data and survival information of bladder cancer tissue of a patient, extracting data from a preset amino acid metabolism related gene set, and constructing a gene expression matrix; then, clustering the patients by adopting an unsupervised clustering algorithm, and determining at least two types of amino acid metabolism molecule subtypes in combination with a stability index; thirdly, carrying out survival difference analysis on different subtypes, screening out differential expression genes related to survival outcomes, constructing a survival prediction model based on the differential expression genes, and calculating amino acid metabolism scores of the patients; finally, the patients are grouped according to the scores, and molecular typing based on the amino acid metabolism characteristics is completed. According to the invention, stable and accurate typing of the bladder cancer patient is realized.

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.