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

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 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 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.

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.

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 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 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 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 invention discloses a method for screening key genes of intramuscular fat traits of pigs. The method comprises the following steps: step 101, constructing a test group and acquiring multi-dimensional phenotype data; step 1011, carrying out accurate determination; step 1021, genome DNA extraction and genetic typing are carried out; step 1022, carrying out genetic typing data quality control; step 1023, selecting a transcriptome sequencing sample and establishing a database for sequencing; step 1024, performing transcriptome data quality control; step 1025, performing transcriptome data comparison and gene expression quantification; step 1031, carrying out whole genome association analysis; step 1032, carrying out differential expression gene analysis; step 1033, carrying out weighted gene co-expression network analysis; step 1034, performing correlation analysis on the WGCNA module and a phenotype; step 1035, determining a network core gene set; and step 1036, determining a key candidate gene. The method has the beneficial effect that the defect of high false positive of single omics analysis is fundamentally overcome.

The invention provides a ginkgo seed toxicity mechanism analysis method based on network toxicology, machine learning and experimental verification, and the ginkgo seed toxicity mechanism analysis method comprises screening and intersection analysis of ginkgo seed toxic component related targets and gastrointestinal tract toxicity related targets. Screening of gastrointestinal toxicity related differential expression genes (DEGs) and weighted gene co-expression network analysis (WGCNA); the method comprises the steps of key target spot selection, key target spot intersection analysis and function enrichment, key target spot screening and verification driven by machine learning, core gene function analysis, molecular docking and molecular dynamics simulation and experimental verification. The method breaks through the limitation that traditional toxicology research focuses on a single component or a target spot, constructs a component-target spot-disease network through a network toxicology method, captures complex interaction between toxic components and a biological system, and comprehensively reveals the multi-component and multi-target spot action characteristics of ginkgo seed toxicity.

The present application relates to the field of plant genetic engineering and molecular breeding technology, in particular to the application of corn ZmTAR4 gene in regulating callus related traits. The present application identifies a key gene ZmTAR4 through whole genome association analysis and weighted gene co-expression network analysis, which encodes a protein with tryptophan amino transferase activity. CRISPR / Cas9 knockout experiment proves that the gene is a gene necessary for positively regulating corn young embryo endogenous auxin (IAA) synthesis and embryogenic callus (EC) induction. Further, through overexpression experiment, it is proved that in corn materials with low induction rate, improving the expression level of ZmTAR4 can significantly improve the induction rate, growth amount and embryonic structure number of embryogenic callus, which fully verifies the positive regulation effect of the gene from the functional gain perspective.