42 results about "Gene co-expression network" patented technology

The invention discloses a key gene identification method related to tobacco nitrogen response, which comprises the following steps: S1, setting four nitrogen fertilizer gradient treatments on the basis of same phosphorus and potassium fertilization by adopting a field experiment of a completely random block; s2, randomly taking the 6th to 8th leaves of the five plants in each area, and dividing a sample into two parts: quickly freezing one part with liquid nitrogen, and storing at-80 DEG C for RNA (Ribonucleic Acid) extraction and transcriptome sequencing; one part is used for measuring the nitrogen content, and after baking and drying treatment, a KjeltecTM8100 automatic nitrogen analyzer is used for measuring; s3, nitrogen content determination: determining the nitrogen content in the treated sample by using an automatic nitrogen analyzer KjeltecTM8100; according to the method, high-throughput transcriptome sequencing and weighted gene co-expression network analysis (WGCNA) are combined, so that not only can gene expression maps of flue-cured tobacco leaves treated by different nitrogen fertilizers be comprehensively captured, but also gene modules with similar expression modes can be mined from a global perspective.

The invention discloses an inference method and an inference system of a gene regulatory network. The inference method comprises the following steps: acquiring transcriptome data and prior gene network data of a single cell; extracting a gene sub-network related to the transcriptome data from the prior gene network data; obtaining a first view and a second view for the gene sub-network according to a first random deletion strategy and a second random deletion strategy; providing the first view and the second view to a neural network model, and performing comparative learning based on the neural network model to obtain incoming features and outgoing features of each node in the gene sub-network; and constructing a regulation score matrix based on the incoming features and the outgoing features, wherein the regulation score matrix displays the regulation association degree between genes in the transcriptome data of the single cell. According to the technical scheme, the direct causal relationship and the indirect association relationship can be effectively distinguished, so that the gene co-expression network more accurately reflects the real regulation relationship.

The invention discloses a prognosis evaluation method and system for diffuse large B-cell lymphoma. According to the method, firstly, a gene module closely related to lipid metabolism is screened from DLBCL transcriptome data through weighted gene co-expression network analysis (WGCNA), and then eight key prognosis genes including FNDC1, IL22RA2, C15orf48, OMD, MFAP2, BC017398, CXCL6 and TNFAIP6 are identified from the module by adopting multi-step regression analysis (single factor Cox, LASSO and multi-factor Cox). A risk scoring model is constructed based on the expression levels and regression coefficients of the genes, and DLBCL patients can be divided into a high-risk group and a low-risk group with significant survival differences. The risk score and the clinical pathological factors are further integrated to construct a column graph, and individualized survival probability prediction can be achieved. The invention further provides a corresponding prognosis evaluation system, electronic equipment and a storage medium. An independent data set verifies that the prognosis model has excellent prediction performance and clinical practical value.

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 application discloses a kind of screening methods and application of pivot gene related to sheep muscle fatty acid content, to solve the technical problems that local sheep breed sample quantity is limited, traditional single gene analysis method is difficult to analyze fatty acid metabolism regulation from network level.This application carries out transcriptome sequencing to multiple months of muscle tissue of Gangba sheep, constructs gene expression matrix, using weighted gene co-expression network analysis (WGCNA) Combined with module characteristic gene and fatty acid phenotype correlation screening strategy, the pivot gene significantly positively correlated with muscle fatty acid content is obtained.The screening method can construct a robust co-expression network under limited sample size, systematically identify the functional module and core gene related to the content of fatty acids such as linoleic acid, and reveal the dynamics of fatty acid metabolism at different ages, and the method can be extended to other plateau livestock;The screened pivot gene can be used as a molecular breeding marker for early selection of Gangba sheep, and the breeding cycle is shortened.

The disclosure provides a kind of integrated energy system multivariate load forecasting method and system considering polyphase coupling, it belongs to garden integrated energy system load forecasting technical field, the scheme includes: obtaining historical load forecasting related data in garden integrated energy system, and carries out corresponding pretreatment;Based on the historical load forecasting related data, the nonlinear relationship between multivariate load and each influencing factor corresponding to the load is mined using the weighted gene co-expression network analysis method, and the influencing factors strongly related to different loads are determined;The characteristics corresponding to the load history data of different loads and their strongly related influencing factors are simultaneously input into the pre-trained load forecasting model, and the load forecasting results corresponding to different loads are obtained;Wherein, the load forecasting model uses MTL framework, uses BiLSTM as the shared layer of MTL, and the prediction tasks under different loads share information through the shared layer.

The invention belongs to the technical field of bioinformatics, and discloses a molecular marker screening method and device based on sequencing, equipment and a storage medium. Sample sequencing data is obtained for quality control processing to obtain clean sequencing data, and gene variation sites are obtained through detection; analyzing to obtain a target gene expression map; carrying out weighted gene co-expression network analysis to obtain an analysis result; carrying out co-expression analysis, constructing a miRNA-gene molecule interaction network, carrying out topology analysis on the miRNA-gene molecule interaction network, and identifying key nodes; and finally, according to the gene variation site, the target gene expression map, the analysis result and the key node of the miRNA-gene molecular interaction network, combining with a machine learning algorithm to calculate the importance score of each candidate marker, and obtaining a specified number of candidate markers with top scores and the combination thereof as a target marker. Therefore, the potential efficiency of the marker can be comprehensively evaluated in multiple dimensions, and the specificity and sensitivity of the screened marker are further improved.

The invention provides application of a biomarker in preparation of a detection product for diabetic sarcopenia, and relates to the technical field of diagnostic products for diabetic sarcopenia. The biomarker is BOLA3, skeletal muscle transcriptome data of a normal individual and a type 2 diabetes patient (T2D) are integrated, after strict quality control and homogenization treatment, differential expression analysis is performed by using a lima packet, and a T2D skeletal muscle differential expression gene set is obtained. Core module genes obviously related to the phenotype of the diabetic sarcopenia are screened out through weighted gene co-expression network analysis. Combining an iron metabolism and ferroptosis related gene set in a database, taking an intersection of the differential gene, the WGCNA module gene and the iron metabolism gene set, and finally positioning 45 hub genes. A random forest algorithm and a neural network algorithm are adopted to finally determine that BOLA3 is a key biomarker of the diabetic sarcopenia, and a new direction is provided for diagnosis and treatment of the diabetic sarcopenia.

The application provides a LiCOMT gene for regulating synthesis of lycoris kingianus glycosides and application thereof, belongs to the technical field of plant genetic engineering, and the nucleotide sequence of the LiCOMT gene is shown as SEQ ID NO. 7, and the amino acid sequence of the encoded protein is shown as SEQ ID NO. 8; the application screens out a candidate gene LiCOMT significantly related to lycoris kingianus glycoside accumulation from lily bulb development period transcriptome data through weighted gene co-expression network analysis (WGCNA), and confirms that silencing the LiCOMT gene can reduce the content of lycoris kingianus glycoside A, lycoris kingianus glycoside B and lycoris kingianus glycoside F in lily bulbs by about 60%, 42% and 43% respectively by using a virus-induced gene silencing (VIGS) technique; and the application provides important gene resources and theoretical basis for lily secondary metabolism regulation and molecular breeding.

This invention provides a method and system for processing hepatocellular carcinoma data. The method utilizes the CIBERSORT tool combined with weighted gene co-expression network analysis to screen a gene set positively correlated with M2 macrophage infiltration. Through differential expression analysis and prognostic correlation analysis, target genes with prognostic value are identified from the gene set. A dataset containing the target genes is acquired, and a comprehensive machine learning algorithm is used to generate several corresponding prognostic prediction models for each dataset. The C-index of the dataset in the corresponding prognostic prediction model is calculated, and target prognostic prediction models are selected based on the C-index and the complexity of the prognostic prediction model. Compared to the traditional method of selecting an algorithm for modeling at the beginning of the study, the final selected target prognostic prediction model reflects more objective and realistic prediction results. Furthermore, the prediction accuracy of this target prognostic prediction model is improved.

This invention discloses a method for screening and regulating environmentally adaptive minor genes during wheat propagation, comprising the following steps: Step S1: Define the target environment and adaptability phenotype, extract key environmental covariates of the target ecological zone, and construct a digital environmental profile; screen physiological and ecological traits corresponding to environmental stress and measure them using high-throughput phenotyping technology; Step S2: Construct genetic materials and design experiments, using a genetic material system composed of a core germplasm bank, a multi-parental design population, and chromosome segment substitution lines, and setting up multi-environment gradient experiments and precise stress control experiments; Step S3: Integrate data analysis and candidate gene mining, integrate environmental covariates, and screen genetic loci that change synergistically with environmental signals; determine gene information strongly correlated with adaptability phenotypes through weighted gene co-expression network analysis; Step S4: Functional verification and mechanism analysis to confirm the environment-specific function of the genes.

Provided is a method for inducing differentiation of human pluripotent stem cells into pancreatic progenitor cells. Compared with conventional induced differentiation processes, the present method not only shortens a process that takes more than 30 days and requires 6-7 stages to only 19 days and 5 stages, but also significantly increases the proportion of β-like cells to 60%–70%, effectively improving hyperglycemic symptoms in diabetic mouse models. Furthermore, single-cell transcriptome detection of the grafts revealed that 60%-70% β cells could be detected, which were more mature than the cell state before transplantation. A method for assessing cell quality is also provided, which is used to evaluate the quality of in vitro induced cells. The method no longer relies on conventional detection based on the expression of a few specific genes and proteins, but uses single-cell omics technology to qualitatively and quantitatively evaluate the characteristics and differentiation efficiency of the induced cells based on a "three-module gene co-expression network," providing a new evaluation standard for the field.

The invention discloses a method for analyzing an action mechanism of a plasticizer acetyl tri-n-butyl citrate (ATBC) for inducing breast cancer based on network toxicology. The method comprises the following steps: firstly, integrating multiple databases to obtain and standardize ATBC targets, and identifying disease-related genes in combination with differential expression of TCGA data and weighted gene co-expression network analysis (WGCNA); overlapping target spots are obtained through intersection of the three, a protein interaction network is constructed, and function enrichment analysis is carried out. TCGA is used as a training set, GEO is used as a verification set, random forest and Lasso regression are combined to screen out core targets MAOA and ADRA2A, and a prognosis model is constructed. And finally verifying that the ATBC can be stably combined with the two target spots through molecular docking (the combination energy is 1t;-5.0 kcal / mol). According to the invention, a full-process scheme from target prediction, function analysis, machine learning screening to molecular docking verification is established, and a standardized normal form is provided for the study of the carcinogenic mechanism of environmental chemicals.

The application relates to the field of bioinformatics and clinical auxiliary decision-making technology, in particular to a high-dimensional weighted membranous nephropathy gene co-expression network construction method, which integrates biomolecular interaction, disease pathways and health baseline data to construct an initial topological matrix, uses a Riemann manifold optimization algorithm to inject high-dimensional transcription sequencing data into the matrix and update edge weights, generates a high-dimensional weighted network of a target biological sample, accurately locks an abnormal gene module by calculating the topological residual error of the network and the health baseline and the dynamic offset of the eigenvector centrality, compares module features with a target point database to screen candidate drug targets, and stores network weight eigenvalues into a vector database, so that gene co-expression network construction and drug target screening based on biological priori and data driving are realized; and the application accurately retains weak but key local nonlinear cascade signals.

The invention discloses a method for discussing a ferroptosis related molecular mechanism and a potential treatment target spot in periodontitis, and relates to the technical field of periodontitis treatment, and the method comprises the following steps: S1, preliminarily screening a periodontitis differential gene by a data set; s2, WGCNA analysis: carrying out weighted gene co-expression network analysis on WGCNA, and importing gene expression data into a WGCNA packet of an R language for analysis; in the invention, a weighted gene co-expression network analysis (WGCNA) method is adopted; wGCNA is an efficient and comprehensive high-dimensional data analysis method, and the accuracy and effectiveness of gene chip data analysis are proved; in addition, the obtained result is verified through an external data set and a periodontitis cell animal model, so that the accuracy of the result is further improved; gSE16134 from a GEO data set is analyzed, and the result shows that CYBB (NOX2) is closely related to occurrence and development of periodontitis.

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 biomedicine, in particular to a sepsis marker screening method based on a peripheral blood mononuclear cell gene co-expression network, and the method comprises the following steps: obtaining a sample; carrying out batch RNA sequencing, differential expression analysis, weighted gene co-expression network analysis, cross analysis and protein interaction network analysis on the sample, and screening hub genes; performing function enrichment analysis and immune cell infiltration analysis on the hub gene, and screening out a core gene; and carrying out expression verification and clinical correlation analysis on the hub gene. Starting from the overall perspective of a gene network, the screened Hub gene has higher biological significance and reliability, through cross screening of WGCNA and differential expression analysis, the range of candidate genes is greatly narrowed, the screening efficiency and accuracy are improved, bioinformatics analysis, scRNA-seq cell localization and protein level experimental verification are integrated, and the screening method has the advantages that the screening efficiency is greatly improved, and the screening cost is reduced. A complete evidence chain is formed, and the credibility of the marker is ensured.

The application discloses a method for eRNA transcription identification, regulation target prediction and function annotation based on high-throughput transcriptome sequencing data, and is characterized in that the method comprises the following steps: identifying part of non-coding RNA in which a transcription start site is located in an enhancer region as eRNA; obtaining eRNA-related protein coding genes which simultaneously exist in an eRNA-protein coding gene co-expression network and an eRNA-centered regulation network, constructing an eRNA-protein coding gene relationship network, and extracting protein coding genes which are directly connected or closely connected to the eRNA, so as to predict potential regulation targets of the eRNA; and finally, performing function enrichment analysis on the potential regulation targets of the eRNA, so as to obtain the results of eRNA function annotation, and the method has the advantages of wider application range, and can be applied to all eRNAs and more accurately obtain the action forms between the eRNA and the protein coding gene.

The invention discloses a fatty acid metabolism regulation gene mining method based on transcriptome data driving, a FindGene platform developed by the invention can efficiently integrate and standardize large-scale RNA-Seq data, systematic mining function related genes are analyzed through a weighted gene co-expression network, and the gene mining efficiency is improved. The limitation of dependence on small-scale experiments and manual screening in the past is broken through, and the efficiency and accuracy of key regulation gene screening are greatly improved. According to the schizosaccharomyces cerevisiae engineering strain QH-7 constructed by carrying out site-directed mutagenesis, overexpression and combined transformation on a key gene obtained by screening, the yield of fatty acid reaches 2.12 g / L and is increased by about 118.6% compared with that of a wild strain, and the biosynthesis efficiency of the fatty acid is greatly improved. According to the modified strain, the total fatty acid content is increased, meanwhile, the proportion of unsaturated fatty acids (C16: 1, C18: 1 and C18: 2) is remarkably increased, and the requirements for high-quality lipid raw materials in the fields of downstream biofuels, nutritional supplements and the like are met.

The invention provides a circadian rhythm evaluation method and system oriented to cardiovascular states, and belongs to the field of transcriptional gene analysis, and the method comprises the steps: obtaining transcriptome data at a single time point; gene expression data are positioned and extracted from the transcriptome data through a preset rhythm homeostasis gene module, and the rhythm homeostasis gene module is constructed through a weighted gene co-expression network analysis algorithm; and calculating the collaboration, the volatility and the network concentration degree of the gene expression data based on a dynamic network biomarker algorithm, and determining the circadian rhythm disorder index of the to-be-evaluated user according to the collaboration, the volatility and the network concentration degree. According to the method, collaborative expression characteristics are systematically captured by using a rhythm homeostasis gene module, dependence on a small number of core clock gene signals is avoided, and quantitative calculation is carried out on the collaboration, volatility and network concentration ratio of gene expression data in the module based on a dynamic network biomarker algorithm; and an objective and quantifiable molecular evaluation index is provided for an individual rhythm state.

This invention discloses an intelligent regulation method and system for treating high-nitrogen and high-phosphorus wastewater from *Chlorella* based on WGCNA and machine learning, belonging to the field of wastewater treatment technology. This invention constructs a time-series experimental system for treating high-nitrogen and high-phosphorus wastewater from *Chlorella*, simultaneously collecting physiological phenotypes and whole-transcriptome sequencing data at multiple time points; it uses WGCNA to construct a gene co-expression network, screening feature modules and candidate gene sets significantly related to the target traits of wastewater treatment; it combines a random forest algorithm to construct a condition classification and phenotype prediction model; and it integrates the results of these two analyses to screen a core hub gene set and construct gene biomarkers, achieving early prediction of wastewater treatment efficiency and intelligent process regulation. This invention can accurately predict the treatment efficiency of *Chlorella* wastewater, significantly improve the treatment efficiency of high-nitrogen and high-phosphorus wastewater, and provide theoretical support and a complete technical solution for the resource utilization technology of microalgae wastewater.

The invention provides a composition for preventing and treating ovarian senescence and a preparation method and application thereof, and belongs to the technical field of ovarian senescence prevention and treatment. The composition for preventing and treating ovarian aging provided by the invention is prepared from radix rehmanniae praeparata, fructus lycii, poria cocos, astragalus membranaceus and perilla frutescens. According to the method, multidisciplinary technologies such as data mining, network pharmacology, weighted gene co-expression network analysis (WGCNA) combined machine learning and molecular docking verification are integrated; the multi-component-multi-target-multi-channel action mechanism of the medicinal and edible composition consisting of prepared rehmannia root, wolfberry fruit, poria cocos, astragalus membranaceus and purple perilla for preventing and treating ovarian aging is systematically illuminated. According to the medicinal and edible composition disclosed by the invention, the release of SASP (senescence-related secretory phenotype) senescence-related factors is slowed down by regulating and controlling a PI3K-Akt / SIRT1-TP53 signal axis, so that the senescence of ovarian cells is delayed, the effect of resisting ovarian senescence is exerted, and a theoretical basis is provided for the research of a modern pharmacological mechanism.

The invention belongs to the field of plant molecular biology and functional genomics, and particularly relates to a method for identifying a barren-resistant index gene of a tea tree based on WGCNA analysis and application. The invention discloses a method for identifying a barren-resistant related gene of a tea tree, which comprises the following steps: firstly, selecting tea tree varieties with different growth vigor, and measuring the root biomass under the same condition; performing low-nitrogen treatment on the seedlings, extracting RNA (Ribonucleic Acid) from root tissues, establishing a cDNA (Complementary Deoxyribonucleic Acid) library, and performing high-throughput sequencing; screening a gene set with high expression and large variable coefficient based on sequencing data, constructing a gene co-expression network by using WGCNA, and identifying a green module highly related to root biomass; 14 core candidate genes which are stably and highly expressed in varieties with strong growth vigor are screened out as barren-resistant potential candidate molecular indexes, are used for germplasm resource evaluation, early screening of low-fertilizer-resistant varieties and molecular marker-assisted breeding, and have important theoretical values and application prospects.

The invention provides a method for screening bee heat stress character related genes based on time sequence transcriptome sequencing and weighted gene co-expression network analysis, which comprises the following steps: carrying out heat stress treatment on capped bees, culturing, and setting a control group; collecting biological characteristics of bees in the heat stress treatment group and the control group, extracting RNA, sequencing to obtain transcriptome sequencing data, and analyzing by comparing with a reference genome to obtain differential expression genes; constructing a gene co-expression network based on the determined soft threshold by using a WGCNA software package, calculating gene expression similarity, and dividing modules; screening out modules significantly related to weight and malformation characters; key genes are screened; and performing Wehn diagram intersection analysis on the module key gene and the differential expression gene, and screening to obtain the Hub gene related to the heat stress character. According to the method, awareness of heat stress response and adaptation of the bee sealing cover is improved, and a direct theoretical basis is provided for analyzing a molecular mechanism of dysplasia caused by heat stress.

The present application relates to the technical field of bioinformatics and oncology, and particularly relates to a tumor driver gene identification system fusing multi-omics data and graph neural network, which comprises a data preprocessing module, a heterogeneity feature fusion module, a dynamic gene graph construction module, an improved graph neural network identification module, and a result verification and optimization module. Through standardizing the multi-omics raw data, the heterogeneity features are weighted and fused by using the attention mechanism, the dynamic gene co-expression network is constructed by combining the tumor development time series data and the clinical information, the driver gene identification is performed by using the graph attention network with embedded residual connection and multi-scale feature extraction, and the closed-loop optimization is realized by using the wet experiment data and the clinical sample feedback. The deep fusion and dynamic modeling of multi-dimensional data are realized, the accuracy, specificity and generalization ability of the driver gene identification are significantly improved, and the present application can be widely applied to the early diagnosis of tumors, the research and development of targeted drugs, and the formulation of individualized treatment plans.

The application relates to the field of precision medicine and artificial intelligence, in particular to a Parkinson's disease auxiliary diagnosis system and method based on a dynamic graph neural network model. The system comprises a micro-service architecture platform constructed based on Springboot, serving as a unified API gateway, and at least comprising a graph neural network model micro-service, a gene graph analysis micro-service and a gene screening micro-service. The application realizes rapid and efficient auxiliary diagnosis by constructing a gene plane filtering network through a multi-scale embedded gene co-expression network analysis technology and combining a dynamic graph neural network model to predict Parkinson's disease. The application also provides an interactive gene network graph, supports visual analysis of 1000 gene nodes and nearly 3000 gene regulation edges, and helps to deeply understand disease mechanisms and formulate individualized treatment plans. The application solves the problems of long cycle, poor accuracy and heavy burden on patients in traditional Parkinson's disease diagnosis methods, and provides an innovative solution for early screening and accurate diagnosis of Parkinson's disease.

The invention discloses a sample typing method, system and equipment for medulloblastoma, and a medium. The method comprises the following steps: acquiring gene expression data and clinical subtype information of a patient with medulloblastoma; constructing a plurality of co-expression modules through a weighted gene co-expression network on the basis of the gene expression data, calculating feature genes of the co-expression modules, screening out target co-expression modules related to the to-be-typed sample on the basis of the feature genes and related scores, and extracting target module genes from the target co-expression modules; carrying out differential expression gene analysis on the to-be-typed sample to screen out differential expression genes meeting a first preset condition, determining subtype candidate genes based on the target module genes and the differential expression genes, and typing the to-be-typed sample of the medulloblastoma based on the state of the FHDC1 gene in the subtype candidate genes to obtain the to-be-typed sample of the medulloblastoma. Therefore, the invention can improve the accuracy of sample typing of myeloblastoma.

The application provides an Alzheimer's disease auxiliary precision identification method and system based on a multi-omics marker network and an adaptive support vector machine. Through a marker network based on five modal data and an adaptive support vector machine model, the precise identification of Alzheimer's disease and other types of dementia is realized. The method fuses multi-source heterogeneous data such as blood, urine, neuroimaging, electrophysiology and clinical evaluation, adopts weighted gene co-expression network analysis to screen specific markers, and realizes cross-modal feature interaction learning through a self-attention mechanism. The online learning mechanism is introduced to enable the model to adapt to new data distribution, and the explainability module is combined to output the contribution of each marker to the diagnosis. Finally, with the help of a cloud platform, multi-center data synchronization and model optimization are realized, which significantly improves the accuracy, specificity, early diagnosis ability and adaptability of the AD diagnosis and identification model, overcomes the limitations of the prior art, and assists doctors in diagnosing and treating Alzheimer's disease.