61 results about "Differentially expressed genes" patented technology

The invention discloses an adversity high-photosynthetic-efficiency transcription factor screening method based on deep learning, and relates to the technical field of biological information analys.The method comprises the steps that rice multi-modal stress response data is obtained and preprocessed, and preprocessed gene expression data is obtained; carrying out differential expression gene screening and co-expression network analysis on the preprocessed gene expression data, extracting multi-modal features, and fusing the multi-modal features to generate a multi-modal input feature matrix; constructing a double-layer deep learning model, training the double-layer deep learning model by using the multi-modal input feature matrix, and respectively outputting a regulation and control relationship matrix of transcription factors and target genes and a regulation and control relationship matrix of transcription factors and target pathways; and according to an output result, calculating a comprehensive score of each transcription factor through a multi-dimensional scoring system, and screening out the stress high-photosynthetic-efficiency transcription factor according to a predetermined screening standard.

This invention discloses a method for constructing a prognostic model for acute myeloid leukemia (AML) based on ferroptosis-related genes. The method involves acquiring gene expression data from AML patients and healthy samples to screen for differentially expressed ferroptosis-related genes (DEGs) associated with AML. Using univariate Cox proportional hazards regression analysis, LASSO regression analysis, and multivariate Cox proportional hazards regression analysis, key genes ACSF2, SLC7A11, DNAJB6, and SOCS1 are selected from these DEGs. A prognostic risk model is constructed based on the expression levels of these key genes and their corresponding multivariate Cox regression coefficients. The risk score formula is: Risk Score = 0.534 × ACSF2 expression value - 0.453 × DNAJB6 expression value + 0.194 × SLC7A11 expression value + 0.308 × SOCS1 expression value. The prognostic model constructed in this invention has high predictive accuracy and reliability, effectively stratifying the risk and assessing the prognosis of AML patients, providing an important reference for the clinical treatment of AML.

The application discloses a method for screening garlic fructan adversity response key enzyme genes and metabolites based on transcriptome and metabolome, and the method can better analyze the relationship between genes and metabolites by jointly analyzing the fructan-related differentially expressed genes measured by the transcriptome and the fructan-related metabolism substances with obvious metabolism changes measured by the metabolome, and constructing a gene-metabolite network interaction diagram.

A single-cell level spatial omics multi-modal fusion method, comprising: extracting differential expression genes and nuclear spatial morphological features from spatial transcriptome data, single-cell sequencing data and histological images, and realizing field adaptation between different platforms by using conditional variational autoencoder. Based on a probability inference model, spatial transcriptome expression, single-cell omics and morphological features are fused to jointly infer the type and gene expression level of each cell. A spatial cell network is constructed by a graph attention mechanism to realize the spatial diffusion and recognition of cell types in the whole slice range. Combined with a multi-omics enhancement module, the unmeasured gene and protein expression are completed based on expression similarity, and the prediction consistency is improved through spatial correction. The method realizes high-resolution reconstruction of single-cell multi-omics information in three-dimensional space, improves the information coverage and spatial resolution of spatial omics data, and provides an efficient and low-cost solution for spatial biology and precision medicine research.

The invention discloses a biopsy muscular fascia sample analysis method and system, and relates to the field of muscular fascia sample analysis. The biopsy myofascia sample analysis method comprises the following steps: acquiring pre-stored RNA data of a plurality of biopsy myofascia sample groups; carrying out RNA-seq high-throughput sequencing on the RNA data of each biopsy myofascia sample group to obtain transcriptome data; performing inter-group difference analysis on the transcriptome data according to a preset comparison group, and extracting a differential expression gene data set; and carrying out BDNF layered enrichment analysis on the differential expression gene data set to obtain a BDNF enrichment significant set of each contrast biopsy myofascia sample group, and carrying out cross validation processing based on the BDNF enrichment significant set of each contrast biopsy myofascia sample group. Therefore, it is effectively ensured that the screened BDNF pathway target spot is highly matched with the pathological characteristics of myofascia lesion and is clearly associated with intervention response, and the potential curative effect of acupuncture intervention is accurately evaluated.

The present disclosure provides techniques for accessing FADS1 activity in a patient. Also provided are techniques for determining the appropriateness of treatment of a patient with a FADS1 modulating (e.g., inhibiting) compound. This determination may be made by analyzing one or more biological indicators of FADS1-mediated disease or disorder in the subject. The one or more biological indicators may include one or more of a ratio of polyunsaturated fatty acids (“PUFAs”) in the subject, a relative abundance of one or more cell types, a relative abundance of one or more differentially expressed genes (“DEGs”) (or gene signatures, such as RNA, for such DEGs), and / or a relative abundance of one or more metabolites. Also disclosed are methods of using FADS1 inhibitors in methods of treating metabolic disorders and obesity.

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.

This invention relates to the field of plant disease biological control technology, and particularly to a Fusarium oxysporum cell wall extract, its preparation method, and its application. The extract is derived from the polysaccharide fraction of the cell wall of Fusarium oxysporum spores or hyphae, and contains at least one pathogen-associated molecular pattern (PAMP) component. This component is a non-protein, thermally stable, species-nonspecific elicitor capable of forming an aqueous dispersion system. The extract of this invention can be recognized by plant PRRs and activate PTI immunity. Rehmannia glutinosa tubers treated with the extract showed a significantly increased germination rate after inoculation with Fusarium oxysporum. It also induces superoxide anion production, upregulates the activities of superoxide dismutase, peroxidase, and catalase, and enriches differentially expressed genes in the MAPK signaling pathway, jasmonic acid metabolism pathway, and phenylpropane biosynthesis pathway. Furthermore, the extract can directly inhibit Fusarium oxysporum colony expansion, conidia production, and mycelial growth. Extracts derived from spores or hyphae show no significant difference in inducing disease resistance, and the raw materials are flexible and easy to industrialize. This extract is derived from the cell wall of the pathogen itself, making it environmentally friendly and free from the risk of chemical pesticide residues. It is suitable for the green control of root rot disease in Rehmannia glutinosa.

The present invention belongs to the technical field of bioinformatics, and relates to a machine-learning-based method for screening cross-differentially expressed genes between lung cancer and COVID-19 infection and correspondingly screening prognostic genes of lung cancer. The method comprises: acquiring cross-differentially expressed genes between lung cancer and COVID-19; screening the cross-differentially expressed genes between lung cancer and COVID-19 infection; using Cox regression and LASSO regression to correspondingly screen prognostic genes of lung cancer; and using a K-M survival analysis method, GO and KEGG enrichment analysis methods and a protein-protein interaction analysis method to analyze screened-out prognostic genes of lung cancer. The method provided herein can process data, and can also identify intricate patterns in the data, and has relatively high sensitivity and specificity, thereby improving the efficiency and accuracy of screening. The technique can be extended to the research of other diseases, and features universality.

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.

The application relates to a screening and determining method of an ischemic cerebral stroke animal model biomarker of qi deficiency and blood stasis syndrome, which integrates proteomics and transcriptomics technologies, systematically screens differentially expressed proteins and genes related to the syndrome, and on the basis, constructs various disease animal models, verifies candidate targets by adopting ELISA, simultaneously combines qPCR technology, multi-dimensionally confirms expression changes in the models, further draws ROC curves, calculates AUC values, sensitivity and specificity and other diagnostic performance indexes, evaluates diagnostic potential, and further knocks down and overexpresses differentially expressed genes, deeply excavates potential biomarkers with diagnostic values, and provides scientific bases for objective diagnosis of ischemic cerebral stroke qi deficiency and blood stasis syndrome and exploration of molecular mechanisms.

The invention provides a method for analyzing and identifying an autophagy biomarker related to atherosclerotic plaque progress, and belongs to the technical field of analysis. Comprising the following steps: (1) data acquisition and processing; (2) identifying differentially expressed genes and autophagy related genes; (3) key gene verification and prediction efficiency evaluation; (4) immune infiltration analysis; (5) single cell RNA sequencing analysis; and (6) expression of the three genes in macrophages. According to the invention, autophagy related genes related to AS are identified, and the expression mode, spatial distribution and immune infiltration characteristics of key differential expression genes are clarified by adopting a multi-omics method. The research result of the invention provides a new insight and potential intervention treatment target for understanding the molecular mechanism of AS.

The embodiment of the invention provides a cell subset division optimization method and device based on a single cell clustering result. The method is applied to the technical field of medical data analysis, and comprises the following steps: performing clustering analysis on single-cell RNA sequencing data according to a preset initial clustering resolution to obtain a plurality of cell subgroups after preliminary clustering; performing differential expression analysis on each cell subset obtained by preliminary clustering to obtain a differential expression gene quantity corresponding to each cell subset; and under the condition that the cell subgroups with the differential expression gene quantity lower than a preset threshold value exist in all the cell subgroups obtained by the preliminary clustering, performing automatic iteration merging on the cell subgroups obtained by the preliminary clustering until the differential expression gene quantity of all the merged cell subgroups is not lower than the preset threshold value. And outputting an optimized cell subset division result. According to the method, the accuracy of cell subset division and the efficiency of single cell data analysis are improved, and the biological rationality and interpretability of an analysis result are improved.

The application provides a screening method for a wheat seed storage-related gene, and belongs to the field of agricultural biotechnology.The screening method comprises the following steps: 1) subjecting seeds of a storage-tolerant variety N38 and a storage-intolerant variety N5 to artificial aging treatment, taking the seed embryos after germination; 2) subjecting the seed embryos to metabolomics analysis to obtain differential metabolites; 3) extracting total RNA of the seed embryos, performing transcriptome sequencing, and screening candidate differential expression genes; and 4) screening the candidate differential expression genes associated with the obtained differential metabolites to obtain final screening differential expression genes.The application subjects known storage-tolerant and storage-intolerant wheat varieties to artificial aging, performs metabolomics detection and transcriptome sequencing, screens differential metabolites and candidate differential expression genes, and further analyzes to obtain a wheat seed storage-related gene, thereby providing key gene resources and technical support for wheat storage-tolerant molecular breeding.

The invention discloses a curative effect prediction marker screening method, a curative effect prediction method and device, a storage medium and a program product. The curative effect prediction marker screening method comprises the following steps: preprocessing sample data; screening out differential expression genes related to treatment response from the preprocessed sample data; constructing a classifier model according to the gene features of the differential expression genes; and evaluating the performance of the constructed classifier model, and selecting a curative effect prediction marker according to an evaluation result.

The invention provides a gene contamination treatment method, electronic equipment and a storage medium, and the method comprises the following steps: determining a correlation coefficient between gene expression information of cells in a to-be-treated sample set and gene expression information of cell types in a reference sample set, and determining a first cell type and a second cell type of the cells in the to-be-treated sample set; determining the types of polluted cells, selecting first cells of which the first cell type is the polluted cell type from the to-be-treated sample set, and taking other cells except the first cells as second cells; determining differential expression genes corresponding to the first cell and the second cell; deleting the differential expression genes from the to-be-processed sample set; and circularly executing the steps until the adjusted to-be-treated sample set meets a preset condition, taking all the differential expression genes as polluted genes, determining polluted cells and non-polluted cells corresponding to the to-be-treated sample set, and removing the polluted genes in the non-polluted cells. By utilizing the method, the cell pollution treatment effect can be improved.

The application provides a method for screening soybean Cercospora sojina blight response genes based on transcriptome analysis, which comprises the following steps: selecting pods of soybean Cercospora sojina blight infected plants and healthy plants planted in the field, performing transcriptome sequencing, screening differential expression genes of soybean responding to Cercospora sojina blight, analyzing biological pathways of the differential expression genes in the Cercospora sojina blight infection process, performing protein interaction analysis, screening interaction core genes, comparing sequences, performing transcript from scratch assembly, analyzing differential variable splicing events and exon usage differences through integration analysis, analyzing post-transcriptional regulation network dynamic characteristics of key candidate genes of soybean responding to Cercospora sojina blight, and screening Cercospora sojina blight response genes of soybean. Four Cercospora sojina blight response genes of soybean are obtained, and expression characteristics in different soybean tissues are analyzed, which provides a theoretical basis for soybean Cercospora sojina blight resistance breeding.

The invention relates to the field of tumor immunology, and discloses an early prediction method and system for the curative effect of triple-negative breast cancer immunotherapy, and the method comprises the steps: extracting the pre-treatment image features of triple-negative breast cancer image data and the post-treatment image features of post-treatment image data, and determining the tumor change state of a target object; the method comprises the following steps: respectively extracting tumor gene expression of the triple-negative breast cancer and normal gene expression of para-carcinoma tissues, carrying out difference analysis on the tumor gene expression and the normal gene expression, extracting an immune related gene set of a human body, intersecting the immune related gene set and differential expression genes, and fitting a risk analysis model of the triple-negative breast cancer; the blood before treatment and the blood after treatment are detected, the gene expression before treatment and the gene expression after treatment are subjected to risk analysis, and the risk change state of the target object is determined; and constructing a curative effect analysis model of the target object, and performing curative effect analysis on the immunotherapy of the triple negative breast cancer. The accuracy of early prediction of the curative effect of triple-negative breast cancer immunotherapy can be improved.

The invention provides a gene influencing sexual precocity of deep-county pigs and a screening method thereof, and belongs to the field of research on sexual maturity of pigs, the gene influencing sexual precocity of deep-county pigs comprises an SRD5A1 gene and / or a CYP11B2 gene, testicular tissues of deep-county pigs before and after sexual maturity are subjected to transcriptome sequencing, differential expression gene functions are analyzed, and the sexual precocity of deep-county pigs before and after sexual maturity is obtained. And screening out candidate genes related to the sexual maturity of the deep county pigs. And then performing transcriptome sequencing on testis tissues of the sexually mature deep county pig and the hybrid pig of the sexually mature deep county pig and the large white pig, analyzing functions of differential expression genes, and screening specific expression genes of the sexual maturity of the deep county pig. Integrating the results of the two times of transcriptome sequencing, analyzing the functions of the coincident genes, and screening candidate genes SRD5A1 and CYP11B2 which influence the sexual precocity of the pigs in the deep county according to the gene functions; and a foundation is laid for subsequent research on precocious puberty of pigs in deep county.

The present application relates to the technical field of plant genetic engineering, and specifically provides the role of protein kinase gene GmCIPK6 in regulating soybean salt tolerance. The GmCIPK6 gene is cloned from soybean, and the GmCIPK6 overexpression and CRISPR / Cas9 knockout transgenic soybean plants are obtained by Agrobacterium-mediated genetic transformation technology. The results show that under salt stress, the leaf damage degree of the overexpression plants is significantly lower than that of the wild type, while the damage degree of the knockout plants is significantly aggravated; at the same time, the activities of antioxidant enzymes CAT, POD and APX in the overexpression plants are significantly increased, while those in the knockout plants are significantly decreased. Transcriptome analysis shows that the differentially expressed genes are significantly enriched in the hydrogen peroxide catalytic pathway and the oxidative stress related pathway. The present application confirms that the GmCIPK6 gene positively regulates the salt tolerance of soybean by regulating the antioxidant enzyme system, and provides an effective gene resource for breeding new salt-tolerant soybean varieties.

This invention relates to the field of bioengineering, specifically to a method, apparatus, electronic device, and storage medium for screening key immune-related genes in calves. The screening method includes: acquiring transcriptome data from peripheral blood samples before and after colostrum in different calf samples; screening differentially expressed gene data based on the transcriptome data from the pre-colostrum and post-colostrum peripheral blood samples, and performing enrichment analysis to obtain gene sets related to specific biological processes, functions, or pathways; and then using weighted co-expression network (WCGNA) analysis to obtain key genes. This invention studies the gene expression regulation patterns and metabolite expression changes in calves before and after colostrum, providing a basis and theoretical foundation for further understanding the establishment and response mechanisms of the calf immune system, when the passive immune system terminates, and how the autoimmune system is regulated at the molecular level.

The invention relates to the technical field of bioinformatics, in particular to a child obesity risk assessment model construction method, device and system, and the method comprises the steps: S1, obtaining a child obesity data set, collecting a lipid metabolism related gene set and a glucose metabolism related gene set, and taking an intersection; s2, performing difference analysis on the children obesity data set by using R packet lima; setting a threshold value of the differential expression gene, and identifying the differential expression gene; s3, data obtained in S2 and data obtained in S1 are intersected, and a differential expression gene set related to lipid metabolism and glycometabolism is obtained; s4, screening key genes through a machine learning algorithm, and determining weight coefficients of the key genes; constructing a risk scoring model; wherein the key genes comprise an APOE gene, an ADM gene and an MAPK14 gene. By screening related genes and fusing a plurality of data, scientific processing of core related gene expression data is realized, doctors are helped to extract useful information from a large amount of data, and clinical work development is assisted.

The application discloses a differential gene identification method based on non-parametric test. Gene expression matrices of an experimental group and a control group are obtained. An MMD unbiased empirical estimation between gene expression level sequences of genes in the experimental group and gene expression level sequences of the genes in the control group is calculated as an original MMD unbiased empirical estimation of the genes. Gene expression levels in the gene expression level sequences of the genes in the experimental group and the control group are combined and then randomly shuffled and rearranged, and an MMD unbiased empirical estimation is calculated as a new MMD unbiased empirical estimation. The significance parameter of the genes is calculated, and the differentially expressed genes are determined according to the significance parameter. The method does not depend on specific data distribution assumptions, can more flexibly process data sets with few time points or large gene expression level fluctuations, is suitable for various types of gene expression data, and improves the flexibility and applicability of analysis.

The invention discloses an application of stevioside in preparation of drugs for preventing immunological liver injury, and experimental results show that administration of stevioside with different concentrations can significantly reduce ALT, AST and ALP levels in mouse serum, inhibit expression of TNF-alpha, IL-6 and IL-1beta, alleviate liver tissue injury and inflammatory cell infiltration, and inhibit oxidative stress. Liver transcriptome GO enrichment analysis finds that differential expression genes are related to oxidoreductase activity. Gene knockout is carried out on the NRF2, so that the protection effect of stevioside on the liver is obviously weakened. The invention discloses the protection effect of stevioside in an immunological liver injury model for the first time, and provides a new safe, natural and effective prevention strategy for immunological liver injury.

The application discloses application of a strigolactone inhibitor in prevention and treatment of TMV viruses and relates to the technical field of molecular biology. The application inoculates TMV after treating Nicotiana attenuata with a strigolactone (SL) hormone, performs transcriptome sequencing, analyzes gene conditions of difference changes, and performs qPCR verification on differentially expressed genes; research on SL-induced promotion of Nicotiana attenuata to TMV infection is carried out, and it is clear that the SL has the ability to inhibit tobacco resistance to tobacco mosaic virus (TMV); it is determined that the SL is a plant hormone for inhibiting plant defense reaction, and the SL inhibitor Tis108 has the ability to enhance tobacco resistance to TMV, and it is determined that the Tis108 is a plant hormone inhibitor for promoting plant defense reaction. The application lays a foundation for further research and utilization of the SL hormone, development of a new type of plant elicitor, safe and efficient prevention and treatment of plant diseases.

This invention discloses a method for screening key genes for rice nighttime heat tolerance based on combined transcriptomic and metabolomic analysis and its application. The method includes: applying nighttime heat stress to rice plants during the grain-filling stage; performing transcriptomic sequencing analysis on rice grains subjected to nighttime heat stress and a control group to screen for genes showing significant differential expression after nighttime heat stress treatment; performing metabolomic analysis on rice grains subjected to nighttime heat stress and a control group to screen for metabolites showing differential accumulation after nighttime heat stress treatment; and performing association analysis between the significantly differentially expressed genes and the differentially accumulated metabolites to screen for genes significantly associated with the differentially accumulated metabolites as key regulatory genes related to rice nighttime heat tolerance. These key regulatory genes can serve as molecular markers for screening rice nighttime heat tolerance and can be used to cultivate rice varieties tolerant to nighttime heat.

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.

This invention provides a set of differentially expressed genes from the m6A cluster for assessing the prognostic survival risk of non-small cell lung cancer (NSCLC), along with its screening method and prognostic risk assessment model; belonging to the field of cancer prognostic risk assessment technology; the differentially expressed gene set from the m6A cluster for assessing the prognostic survival risk of NSCLC includes the following genes: S100A10, IGFBP1, SLC52A1, KREMEN2, SCPEP1, COL4A3, GSTM2, STRAP, PCDH7, PAK2, CYP4B1, MTUS1, ESPL1, PRRX2, TTK, COL4A4, CCR7, NPC2, and SKA1. Based on the above-mentioned differentially expressed gene set from the m6A cluster, this invention constructs a survival risk scoring model, providing new insights for predicting the prognosis of NSCLC.

The invention provides a triple-negative breast cancer pyroptosis related prognosis model and a construction method and application thereof, and the construction method of the model comprises the following steps: (1) obtaining transcriptome data and prognosis data of triple-negative breast cancer patients and normal people from a database, and analyzing to obtain triple-negative breast cancer differential expression genes; and (2) screening from the database to obtain an intersection of the pyroptosis related genes and the differentially expressed genes, based on the genes in the intersection, screening through Lasso Cox regression analysis to obtain genes related to triple negative breast cancer prognosis, and using the genes to construct a risk scoring model. The model can effectively predict the prognosis of the triple negative breast cancer patient through risk scoring.

The invention relates to a method for determining path data influencing the progress of malignant blood particle deficiency type sepsis. The method comprises the following steps: filtering initial gene sequence data to obtain clean gene sequence data; performing gene quantitative analysis on the clean gene sequence data to obtain gene expression quantity data; performing gene differential expression analysis on the gene expression quantity data to obtain differential expression gene data; and performing function enrichment analysis on the differential expression gene data to obtain significant enrichment pathway data. By adopting the method, the path data can be determined to have novelty and specificity.