223 results about "Differentially expressed genes" patented technology

A spatial omics multi-modal fusion method under a single cell level comprises the following steps: extracting spatial morphological characteristics of differential expression genes and cell nucleuses from spatial transcriptome data, single cell sequencing data and histological images, and realizing field adaptation among different platforms by using a conditional variation auto-encoder. And based on a probability inference model, fusing spatial transcriptome expression, unicellular omics and morphological characteristics, and jointly inferring the type and gene expression level of each cell. A spatial cell network is constructed through a graph attention mechanism, and spatial diffusion and recognition of cell types in a full slice range are realized. In combination with a multi-omics enhancement module, undetected gene and protein expression is completed based on expression similarity, and prediction consistency is improved through spatial correction. According to the method, high-resolution reconstruction of single-cell multi-omics information in a three-dimensional space is realized, the information coverage and spatial resolution of spatial omics data are improved, and an efficient and low-cost solution is provided for spatial biology and precise medical research.

The invention provides a key node identification method for a disease marker expression regulation network, and belongs to the technical field of disease markers, and the method comprises the steps: firstly carrying out the preprocessing and quality control of original data, including batch effect removal, abnormal sample identification and the like; then identifying differential expression genes through multiple difference analysis and a pre-training model, and constructing a gene expression correlation network; and integrating multi-source regulation and control data to construct a multi-level weighted network, calculating network node features, and carrying out representation learning and module division. And based on multi-dimensional features such as network topology features, module contribution degree and biological importance, a neural network model is trained to carry out key node identification. And finally, optimizing the model through multi-layer verification such as pathway enrichment, disease gene overlapping, expression stability, time sequence change and network disturbance, and finally obtaining a verified key node set. The problem that in the prior art, the interaction relation between molecules is ignored, and consequently some key regulation and control nodes are possibly missed is solved.

The invention relates to the technical field of single cell data processing, in particular to a database visualization method based on single cells. The method comprises the following steps: acquiring a single cell data set; standardizing the single cell data set to obtain standardized single cell data; performing high-dimensional feature extraction on the standardized single-cell data to obtain single-cell high-dimensional feature data; and performing nonlinear dimension reduction processing on the single-cell high-dimensional feature data to obtain single-cell dimension reduction data. Performing data clustering on the single cell dimension reduction data to obtain single cell clustering data; performing gene differential expression extraction on the single cell clustering data to obtain differential expression gene data; multi-modal data fusion analysis is carried out on single cell data through a data processing technology, a feature mapping technology and a visualization technology; and dynamic weight division is carried out on the single cell database, so that the effect of visualization of the single cell database is enhanced.

The invention discloses an experimental method for relation between cytokine level and tumor immune microenvironment in circulation, which comprises the following steps of: S1, collecting colorectal cancer patient data and normal data for contrast, and preferably screening a gene expression data set from a Gene Expression Omnibus (GEO) database; according to the method, a plurality of gene expression data sets from the GEO database are collected and analyzed, so that differential expression genes between colorectal cancer and normal tissues can be systematically identified, and a solid foundation is provided for subsequent research. The process not only covers batch correction, normalization and batch effect elimination of data, but also visually presents data distribution changes through principal component analysis, ensures the accuracy and reliability of analysis results, confirms that IL6 and IFN gamma are one of the most critical factors affecting the colorectal cancer immune microenvironment, and participate in formation of immune infiltration.

The invention discloses a method and a system for intelligently analyzing heterogeneity of endothelial cells in atherosclerosis. The method comprises the following steps: acquiring sample data of the endothelial cells in the atherosclerosis of a patient; performing feature extraction on the sample data of the endothelial cells to obtain gene expression features of the endothelial cells; dividing the endothelial cells into different subgroups according to gene expression characteristics of the endothelial cells and gene expression similarity so as to determine marker genes for distinguishing the different subgroups, and defining and representing characteristics of each subgroup by analyzing expression modes of the marker genes; gene expression differences among different endothelial cell subgroups are compared, differential expression genes of the related endothelial cell subgroups, which are significantly up-regulated or down-regulated in the atherosclerosis process, are identified, and the differential expression genes are used as key molecular markers for understanding functional changes and heterogeneity of endothelial cells. According to the method, the heterogeneity of the endothelial cells in the atherosclerosis can be accurately analyzed.

The invention relates to the technical field of machine learning, and discloses a cancer treatment effect prediction method and device, equipment and a storage medium. The method comprises the following steps: carrying out radiomics feature extraction on a CT scanning image to obtain radiomics features; performing interested pathological feature extraction based on an attention mechanism and local pathological feature extraction based on an interested pathological feature extraction result on the pathological image to obtain pathological omics features; performing differential expression gene analysis on the transcriptomics data, and performing differential expression gene screening based on a differential expression gene analysis result to obtain transcriptomics characteristics; and inputting a fusion feature obtained by fusing the radiomics feature, the pathological omics feature, the transcriptomics feature and the clinical data into a cancer treatment effect prediction model to obtain a cancer treatment effect prediction result. According to the embodiment of the invention, the cancer treatment effect is predicted by combining the radiomics characteristics, the pathomics characteristics, the transcriptomics characteristics and the clinical characteristics, and the prediction accuracy can be improved.

The invention discloses a colorectal cancer prognosis model construction method based on fatty acid metabolism related genes, and belongs to the technical field of biomedicine, and the colorectal cancer prognosis model construction method comprises the following steps: S1, obtaining transcriptome data of a colorectal cancer diagnosis individual from a TCGA database; s2, performing difference analysis on fatty acid metabolism related gene expression data of the colorectal cancer tissue and the normal tissue, and screening out differentially expressed genes; s3, performing single-factor Cox regression analysis on the differentially expressed genes, and screening out genes related to colorectal cancer prognosis; according to the construction method of the colorectal cancer prognosis model based on the fatty acid metabolism related genes, provided by the invention, the tumor immunotherapy reactivity is evaluated by introducing TIDE scores, and the prior art is improved, so that the immune escape potential and the immunotherapy effect of tumor subtypes are analyzed. The prognosis of a colorectal cancer patient is effectively predicted, and a new molecular marker is provided for personalized treatment of the colorectal cancer.

The invention provides a gene module analysis method and device, equipment and a storage medium, and relates to the technical field of space transcriptomics. The method comprises the steps of obtaining space-time group chip expression data, performing linearization processing, and performing sliding window cutting to obtain a plurality of window units arranged according to a space sequence; calculating gene function activeness and constructing a matrix; clustering and grouping are carried out to obtain window groups; performing difference analysis screening on the window groups to obtain a difference expression gene set; and performing gene module analysis to obtain a functional gene module. According to the method, through an integrated processing flow of structure linearization, sliding window cutting, activeness quantification, clustering analysis and module identification, fine extraction and function reconstruction of spatial expression information in the curled tissue are realized under the condition that an additional algorithm or external data is not introduced; and the practicability and research value of the spatial omics data in a complex organization structure are improved.

The invention is applicable to the technical field of molecular biology, and provides a macrophage related gene KCNA3 and application thereof. The invention discloses a macrophage related gene KCNA3 for the first time, and the gene is identified as a macrophage related differential expression gene of a patient with type 2 diabetes mellitus with carotid atherosclerosis, and shows high expression in the patient with type 2 diabetes mellitus with carotid atherosclerosis. Through analysis of a public database transcriptome chip and high-throughput sequencing data and further verification of CIBERSORTX and WGCNA algorithms, close association between the KCNA3 gene and the macrophages is confirmed. Based on differentiated expression of the KCNA3 gene and correlation of the KCNA3 gene and macrophages, the gene can be used as a potential treatment target for further treating patients with the diabetes mellitus with the carotid atherosclerotic plaque, so that a new mechanism participating in disease progression of the diabetes mellitus with the carotid atherosclerotic plaque is disclosed.

A disease marker expression regulatory mechanism prediction method comprises the following steps: acquiring gene expression data, performing quality control and standardization processing on the gene expression data to obtain standardized gene expression data, identifying differentially expressed genes as candidate markers, and predicting a disease marker expression regulatory mechanism. A transcriptional regulation and control network of the candidate markers is constructed, transcription factor data are generated, a multi-layer neural network model is established through a four-quadrant neural network model, and the four-quadrant neural network model comprises a contribution value calculation layer, a statistical test layer, a quadrant division layer, a feature integration layer and a comprehensive output module. The four-quadrant neural network model adopts an adaptive quadrant division optimization equation set to carry out feature analysis and prediction on the candidate markers; compared with the prior art, the method is remarkably improved in the aspects of feature integration, dynamic modeling, self-adaptive optimization and the like, and important theoretical basis and technical support are provided for disease diagnosis, prognosis evaluation and treatment strategy formulation.

The invention discloses a rape grain weight regulation gene NGAL3 based on whole genome screening and a breeding application thereof, based on the seed size and grain weight phenotypic value of a cabbage type rape germplasm resource, a candidate gene interval is positioned by using GWAS; the method comprises the following steps: selecting large-grain-weight and small-grain-weight extreme phenotypic materials, performing transcriptome sequencing in a critical period of seed development, and screening differential expression genes; and performing cross comparison on the GWAS candidate gene and the differential expression gene to obtain a key gene for jointly regulating and controlling the size and the grain weight of the seed, and performing functional verification on the key gene. The thousand seed weights of the created homozygous three mutant strains L1-sg1-1-4-5 and L2-sg1-4-8-16 are obviously increased by 46% and 29% compared with those of the wild type strains. The invention provides a new target for high-yield rape breeding, and non-transgenic high-grain-weight germplasm can be created through gene editing or beneficial allelic variation of NGAL3 is selected and enriched under the assistance of molecular markers.

The invention relates to a KASP molecular marker of a wheat quality related gene TaSF3B4 and application of the KASP molecular marker. The KASP molecular marker is KASP-TaSF3B4, and an SNP (Single Nucleotide Polymorphism) site (A / G) is detected at 248421161bp of a wheat 3B chromosome, namely, the distance between a TaSF3B4 promoter region and an initiation codon is 1127bp. According to the marker, a TaSF3B4 gene influencing the wheat quality character is obtained according to a way that differentially expressed genes in 15d transcriptome data of a grain extreme material are extremely remarkably enriched to a spliceosome, and regular SNP loci exist through sequence comparison and analysis of the TaSF3B4 genes in different varieties and upstream and downstream sequences of the TaSF3B4 genes. The functional KASP molecular marker of the wheat poly (adenylic acid binding protein) gene TaSF3B4 provided by the invention can be used for identifying whether TaSF3B4 excellent alleles exist in wheat varieties / strains or not, and can be applied to assisted selective breeding and pyramiding breeding of other genes related to known quality.

The invention discloses a screening method of sicca syndrome markers based on conjunctival blotting, and relates to the technical field of biomedicine, and the screening method comprises the following steps: S1, conjunctival noninvasive sampling; s2, RNA (Ribonucleic Acid) sequencing and pathway screening; s3, machine learning modeling is carried out; s4, clinical verification. According to the invention, biomarker screening is realized, and similar biomarker screening can also be realized through tear sample analysis or other ophthalmic samples; however, the alternative schemes have the defects of relatively strong invasiveness, complex operation and the like; sampling is carried out through the conjunctival imprinting technology, invasive operation such as biopsy and blood drawing is avoided, and pain and discomfort are reduced; differential expression genes can be accurately identified, and the sensitivity and the accuracy are relatively high; through immune cell infiltration analysis, the relationship between the immune cell subpopulation and the sicca syndrome related gene is disclosed, a new perspective is provided for a disease mechanism, and a theoretical basis is provided for a future immune regulation strategy.

The invention belongs to the technical field of medicines, and particularly relates to application of BMP9 as an intervention target in preparation of a medicine for treating diabetic nephropathy. More specifically, the invention provides application of a BMP9 accelerant in preparation of a medicine for preventing and / or treating diabetic nephropathy. The BMP9 accelerant is a substance shown in the following (i) or (ii): (i) a substance for increasing the content of BMP9 in a subject; (ii) a substance that increases BMP9 activity in a subject. According to the invention, a DKD mouse model is constructed, and liver RNA-seq sequencing is carried out, such that a differential expression gene BMP9 exists between a normal mouse and a DKD mouse. It is found that BMP9 can be used as a new target for prevention and treatment of DKD and has important theoretical significance and potential clinical application value.

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.

The invention provides application of an IFI16 gene overexpression biological material in preparation of an anti-dengue virus drug, and belongs to the technical field of anti-dengue virus. According to the invention, differentially expressed genes after host cells transfect viruses are found, and the differentially expressed genes are analyzed, so that the key genes related to dengue virus resistance are determined; it is determined that the over-expressed IFI16 gene has the effect of inhibiting dengue virus replication and can be used for preparing the anti-dengue virus medicine.

The invention discloses a chenopodiaceae plant kochia scoparia drought-resistant physiological detection method, and relates to the technical field of drought-resistant physiological detection.The method comprises the steps that S1, samples are prepared and collected; s2, gene expression analysis; s3, carrying out molecular biological verification; and S4, data analysis and conclusion. According to the chenopodium plant kochia scoparia drought resistance physiological detection method, gene expression changes of the kochia scoparia under drought stress are analyzed through gene expression analysis, differentially expressed genes under the drought stress are screened out, and the relationship between the functions of the differentially expressed genes and drought resistance is verified through molecular biology verification; according to the gene expression data and the molecular biology verification result, the conclusion of the drought resistance of the kochia scoparia is obtained, the purpose of physiological detection of drought resistance of the kochia scoparia is achieved, the whole detection process is accurate and efficient, the expression conditions of a large number of genes can be detected at the same time, the detection efficiency is greatly improved, and the drought resistance and adaptability of the kochia scoparia are evaluated more accurately.

The present disclosure relates to methods for diagnosing and treating patients with neurovascular disease including, but not limited to determining a differentially expressed gene in a subject with a disease and methods for treating a subject with a disease based on a differentially expressed gene. Also disclosed are methods for treating a subject suspected of having a stroke.

The invention discloses application of a mitochondrial differentiation expression gene in preparation of a depression diagnostic agent and a depression treatment drug, and relates to the technical field of depression diagnosis and treatment. And the mitochondrial differentiation expression gene comprises one or more of TFAM (Transforming Factor Amplification Molecular), SURF1 and NDUFB9. According to the present invention, the SURF1 gene simultaneously expressed in inhibitory neurons and excitatory neurons is focused on, the key effect of the SURF1 in CUMS-induced depression-like behaviors is found, the CUMS-induced molecular and behavior changes are effectively reversed by the overexpression of the SURF1, and the potential anti-depression effect is displayed; besides, animal experiments find that overexpression of the SURF1 can effectively recover the function of an electron transport chain, ATP generation is enhanced, mitochondrial membrane potential is maintained, and oxidative stress is reduced. The effects jointly relieve depression-like behaviors induced by CUMS, and further support the protection effect of the SURF1 in a depression model, which provides evidence for the new effect of the SURF1 in the pathophysiology of depression.

The invention discloses a method for screening potential treatment targets of acrolein-induced retinal injury, and relates to the field of acrolein toxicity prediction. According to the method disclosed by the invention, a retinal injury related differential expression gene and an acrolein related target spot are obtained, an intersection is taken, protein interaction relationship network analysis is carried out, and a potential mechanism of acrolein-induced retinal injury is predicted. GO functional clustering analysis results show that acrolein is mainly subjected to biological processes such as apoptosis signal channel regulation and epithelial cell development, and KEGG key signal channel enrichment analysis results show that acrolein may induce retinal injury through IL-17 signal channels, human cytomegalovirus infection and cell senescence cell signal channels. The predicted key genes comprise CXCR4, EDN1, ITGA6 and PECAM1, and a new target is provided for subsequent prevention and treatment of acrolein-induced retinal injury.

This application relates to the field of machine learning technology, and discloses a method, apparatus, device, and storage medium for predicting the efficacy of cancer treatment. The method includes: extracting radiomics features from CT scan images to obtain radiomics features; extracting pathological features of interest from pathological images based on an attention mechanism and extracting local pathological features based on the results of the extracted pathological features of interest to obtain pathomics features; performing differentially expressed gene analysis on transcriptomics data and screening for differentially expressed genes based on the results of the differentially expressed gene analysis to obtain transcriptomics features; and inputting the fused features obtained by fusing radiomics features, pathomics features, transcriptomics features, and clinical data into a cancer treatment efficacy prediction model to obtain a cancer treatment efficacy prediction result. The embodiments of this application combine radiomics features, pathomics features, transcriptomics features, and clinical features to predict cancer treatment efficacy, which can improve prediction accuracy.

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 invention relates to the technical field of biology, in particular to a gene co-expression network-based sepsis marker screening method and application. The screening method comprises the following steps: screening differential expression genes; performing hierarchical clustering analysis on each differential expression gene to combine each differential expression gene into a plurality of target gene modules; and screening out hub genes from each target gene module. According to the method, gene expression data and clinical feature data are combined, the accuracy of screening sepsis marker genes is improved, possible abnormal genes irrelevant to the pathophysiological process of sepsis are avoided, gene modules are divided more finely through primary clustering analysis and secondary clustering analysis, GO function analysis is combined, and the accuracy of screening the sepsis marker genes is improved. According to an analysis result, selection of a GO term subset is optimized, and a step length set by primary clustering is corrected, so that the fine granularity and accuracy of analysis are improved, division of gene modules is optimized, and more refined treatment and screening of markers of sepsis are realized.

The invention relates to application of an ILF3 gene in diagnosis or treatment of sepsis, and belongs to the technical field of biological medicines. It is found for the first time that the ILF3 gene is a macrophage-related differential expression gene of sepsis, the expression level of the ILF3 gene in plasma and alveolar lavage fluid of sepsis patients is obviously higher than that of healthy people, a primer pair capable of specifically recognizing and detecting the ILF3 gene is designed, a mouse with the ILF3 gene in macrophage being specifically knocked out is constructed, and the ILF3 gene in the macrophage is specifically knocked out. The invention also designs si-ILF3 capable of specifically interfering with the expression level of the ILF3 gene. The ILF3 gene is used as a sepsis diagnosis marker and a sepsis treatment target, the application of the ILF3 gene detection reagent in preparation of sepsis diagnosis products and the application of si-ILF3 in preparation of sepsis treatment drugs are provided, and the si-ILF3 gene detection reagent is used for further diagnosis and treatment of sepsis patients.

The invention discloses a method for researching influence on hemolytic activity of marine microalgae based on a transcriptome technology, and relates to the field of hemolytic activity analys.The method comprises the steps that pretreatment is conducted on the marine microalgae based on culture requirements, and cystic morphological cell density and hemolytic activity of the marine microalgae are measured according to experimental design rules after pretreatment is completed; carrying out transcriptome sample collection operation by utilizing the marine microalgae subjected to pretreatment, constructing a library according to a transcriptome sample collection result, and obtaining a gene function annotation and a differential expression gene result; the growth conditions of the marine microalgae under different temperature conditions are analyzed according to cystic morphological cell density and hemolytic activity, and the regulatory gene influencing the hemolytic activity of the marine microalgae is obtained by combining gene function annotation and differential expression gene results. According to the method, the hemolytic activity of the marine microalgae cultured under different temperature conditions is extracted, so that the aim of researching related metabolic pathways possibly participating in toxin synthesis and hemolytic activity regulation is fulfilled.

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.

The present invention belongs to the field of gene expression data analysis, and particularly relates to a method for constructing a graph neural network data set based on gene expression; it includes obtaining gene expression profile data of a disease and extracting RNA data, processing the RNA data to generate an RNA expression matrix; performing standardization processing and differential analysis on the generated RNA expression matrix to obtain a differential expression matrix; using WGCNA to analyze the differential expression matrix and construct an overlapping topological matrix; exporting all edge information and node information in the overlapping topological matrix, and encoding the exported information; screening the encoded information, using the screened information to construct a graph neural network data set, and identifying biomarkers according to the graph neural network data set; the present invention performs WGCNA analysis on differentially expressed genes, pays attention to the relationship between genes, and constructs a graph neural network data set by screening node and edge information, which is beneficial to discovering biomarkers of tumors.

An information processing device executes processing of detecting a differential expressed gene that exhibits a specific expression with respect to a cell characteristic of interest, based on gene expression level data of a cell population in which a plurality of subtypes are mixed, and the information processing device includes a processor in which the processor assigns a cluster to which each sample of two groups obtained by dividing the cell population in accordance with the cell characteristic of interest is estimated to belong in a distribution of gene expression levels, to each sample, for each of a plurality of candidate genes that are candidates for the differential expressed gene, and searches for a first probability distribution that fits the distributions of the gene expression levels of the two groups for each of the plurality of candidate genes, based on an assignment result of the clusters.

The present invention discloses a method and system for mining potential proto-oncogenes based on the three-dimensional structure of chromatin, which relates to the field of computational biology technology. The method comprises: obtaining multiple mutation insulation regions based on chromatin data and cancer mutation data, and inputting the regions into a trained binding site predictor to obtain prediction results and destroyed insulation regions; obtaining multiple differentially expressed gene sets based on cancer gene expression data, intersecting the multiple differentially expressed gene sets to obtain a final differentially expressed gene set; intersecting the final differentially expressed gene set and the genes in the destroyed insulation regions to obtain intersection genes; performing survival analysis to obtain analysis results, screening the intersection genes based on the analysis results to obtain genes associated with poor prognosis; and screening the intersection genes to obtain potential proto-oncogenes. This improves the accuracy and reliability of mining potential proto-oncogenes while saving time and economic expenses.

The present invention is applicable to the field of genetic engineering technology and provides a cloning and application of the Sophora alopecuroides (Solanum sophora) SaPEI7 gene. By screening differentially expressed genes from transcriptome sequencing of Sophora alopecuroides under simulated stress conditions, a Sophora alopecuroides gene related to salt tolerance was identified through bioinformatics analysis as the Sophora alopecuroides (Solanum sophora) pectin methylesterase inhibitor protein gene SaPEI7. Quantitative detection using RT-PCR technology revealed a significant increase in gene expression under salt stress conditions. The gene was functionally verified by constructing a plant expression vector and successfully transformed into Arabidopsis thaliana. The results showed that overexpression of the gene in Arabidopsis thaliana improved its salt tolerance, providing a new resource for improving crop stress resistance through genetic engineering technology.