97 results about "Gene sets" patented technology

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 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 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 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 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 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 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 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.

The application relates to a single cell recognition method and a storage medium, wherein the single cell recognition method comprises the following steps: obtaining single cell original gene expression data, a gene label control table and pre-training prior gene feature data; obtaining correction data based on the single cell original gene expression data, generating a cell supervision label, combining the gene label control table to construct a single cell gene set activity matrix; generating a training data set based on the above matrix, label, prior data and correction data, inputting the training data set into a classifier and a reconstruction module of an initial cell deep learning network, obtaining a classification result and a constructed activity matrix; obtaining a total loss by weightedly combining a label loss and a reconstruction loss, iteratively training a gradient in a reverse direction, and generating a cell deep learning network; inputting the correction data into the network, inferring a cell fusion embedding result, and further obtaining a single cell recognition result. Through the application, the problem of low single cell recognition accuracy is solved.

According to the metabolism-oriented clustering-based tumor-associated macrophage metabolism typing method provided by the invention, the single-cell RNA sequencing data set for analysis is collected and arranged, and then the tumor-associated macrophage population is identified and extracted from the single-cell RNA sequencing data set, so that the technical deviation is eliminated; a core metabolism gene set is constructed based on a tumor-associated macrophage population, then weight distribution and optimization of core metabolism genes are carried out, contribution of the metabolism genes in clustering analysis is enhanced, metabolism-oriented clustering is carried out based on the optimized weight, and a metabolism typing result is generated. Therefore, enhancement of metabolic gene expression characteristics and more accurate distinguishing of TAMs subgroups are achieved, typing results are analyzed and verified through GSEA enrichment analysis or GO enrichment analysis, typing categories are output, challenges confronted when high-dimensional single-cell data are processed through a traditional method are effectively overcome, the method is not only suitable for TAMs, but also can be expanded to other tumor-related cell types, and the method has a good application prospect. And the blank in the prior art is filled.

The invention discloses a biomarker prediction system based on multi-omics data integration, a storage medium and a method, and relates to the technical field of data processing and bioinformatics, the system is used for performing gene mutation, CNV difference and transcriptome expression profile analysis on feature data, identifying differential expression genes related to biomarker states, and predicting biomarkers according to the differential expression genes. Obtaining a differential expression gene set; based on the gene set, an XGBoost algorithm is adopted to construct a prediction model, grid search and K-fold cross validation are combined to optimize model parameters so as to improve generalization ability and prevent overfitting, quantitative interpretation is performed on model feature importance by means of an SHAP algorithm, and an optimal key gene feature combination is screened out through multiple training validation to serve as a biomarker prediction panel. The technical effect of automatically discovering small and precise biomarker combinations from complex multi-omics data in a high-stability and high-interpretability mode is achieved, and a transparent and reliable basis is provided for clinical decisions.

Provided are a tissue tracing method for cell-free DNA, a tissue lesion risk notification method, a cell-free DNA-based cancer risk indication method, a cell-free DNA-based gestational disease risk indication method, a cell-free DNA-based receptor tolerance risk notification method, a disease treatment effect evaluation or prognosis method and apparatus, an electronic device, a computer-readable storage medium, a computer program product and a computer program. The tissue tracing method for cell-free DNA comprises: acquiring a tissue-specific expression gene set of a tissue; acquiring first distribution information of cell-free DNA derived from a sample on specific regions of genes in the tissue-specific expression gene set; and, on the basis of the first distribution information, acquiring a tissue contribution index of the tissue in the cell-free DNA, so as to trace the tissue of the cell-free DNA, wherein the specific regions comprise transcription start site (TSS) regions.

A polynucleotide, a kit, an isothermal detection method and a method for predicting premature birth, the method for predicting premature birth comprising: analyzing the expression profile of any one or at least two gene sets in a maternal sample, and predicting whether the subject will have premature birth according to the expression profile. The present application detects the expression amount of the marker gene in the serum of pregnant women for the first time, which is used for the prediction of premature birth risk of pregnant women without clinical symptoms. The main advantage is serological detection, no need to make an appointment in advance like ultrasound, non-invasive detection, no need for vaginal or cervical sampling, and good detection process experience.

The present application relates to a tumor prognosis evaluation gene set and its application, the evaluation gene set is composed of 5 genes, the 5 genes are as follows: MKI67 , PCNA , VEGF-A , MMP9 , TRIM11 , the tumor is selected from any one of colorectal cancer, lung cancer, pancreatic cancer.Compared with the prior art, the present application constructs a multi-type tumor prognosis prediction model MKI67 , PCNA , VEGF-A , MMP9 , TRIM11 Synergistic effect is carried out from four core pathways of tumor proliferation, angiogenesis, invasion and metastasis, abnormal regulation, the detection accuracy of single index is significantly improved, and it is suitable for colorectal cancer, lung cancer and pancreatic cancer.

The present invention relates to a method of prognosis of a subject affected by bladder cancer comprising determining the expression level of a set of genes in an isolated sample from said subject and to kits for performing said method.

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

The invention discloses a method for evaluating the influence of human interference on reproductive health of wild animals. The method comprises the following steps: acquiring steroid hormone species and intestinal microorganism metagenome assembly genomes (MAGs) of a sentry animal excrement sample; based on the steroid hormone type, a first gene set is determined, the first gene is a metabolic enzyme gene, and metabolic enzyme participates in steroid hormone metabolism; determining a first set of coding sequences (CDSs) based on the first set of genes and the MAGs, the first CDS being from the MAGs and a coding region of the first gene comprising the first CDS; determining a first set of microorganisms (HCBs) based on the first set of CDSs, the HCBs being a set of host bacteria (HCB) of the first CDSs, the HCBs being selected from the sentry animal gut microbiota; and based on the total relative abundance of the HCBs, determining whether the human interference affects the reproductive health of the wild animals. The method is scientific and reliable, and provides a scientific decision basis for wild animal protection.

The application discloses a kind of based on PBMC single-cell sequencing multi-cancer risk prediction model construction method and its application, belong to biotechnology field.The method is by integrating multi-source single-cell data, obtains high-quality immune cell atlas by quality control, batch correction and cell annotation, finally by self-test single-cell data queue is verified.Pseudo batch analysis and differential expression screening are used to obtain cell characteristic genes consistent across cancer species, and then LASSO regression dimensionality reduction is performed to finally determine a set of 39 key characteristic genes.Based on this, the screening model exhibits high accuracy (92.2%-97.3%) in both training and independent validation, and can evaluate the risk of 15 cancers at once.The model only needs a small amount of peripheral blood, adapts to conventional commercial scRNA-seq platform, has the advantages of non-invasive, high generalization, strong specificity and good interpretability, and is suitable for large-scale early screening in clinical practice, and has clear conversion prospect.

The application discloses a method for evaluating the influence of human interference on the reproductive health of wild animals. A steroid hormone species and an intestinal microorganism macro-genome assembly genome MAG of a sentinel animal fecal sample are obtained; based on the steroid hormone species, a first gene set is determined, the first gene set comprising a first gene, the first gene being a metabolic enzyme gene, the metabolic enzyme participating in steroid hormone metabolism; based on the first gene set and the MAGs, a first coding sequence CDS set is determined, the first coding sequence CDS set comprising a first CDS, the first CDS being from the MAGs, and the coding region of the first gene comprising the first CDS; based on the first coding sequence CDS set, a first microorganism set HCBs is determined, the HCBs being a collection of host bacteria HCBs of the first CDS, the HCBs being selected from the intestinal flora of the sentinel animal; based on the total relative abundance of the HCBs, it is determined whether human interference affects the reproductive health of wild animals. The method is scientific and reliable, and provides a scientific decision basis for wild animal protection.