14 results about "Coexpression network" 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.

The present application relates to the field of crop genetic breeding and molecular biology, and particularly relates to a highland barley beta-glucan screening index system and a major gene positioning method, which comprises the following steps: firstly, a three-dimensional coupling screening system of genetic stability, functional activity and agronomic adaptability is constructed, the index combination weight is determined by using an entropy weight method and an analytic hierarchy process, and an AMMI model is combined to correct the environmental effect and calculate the comprehensive score of the germplasm; secondly, a recombinant inbred line and a natural germplasm double positioning population is constructed, and near-infrared spectroscopy is combined with a Transformer model to quickly obtain phenotype data; thirdly, a candidate major effective segment is obtained through double population joint positioning, a major effective gene is screened out by combining a transcriptome and a metabolome co-expression network, and a functional molecular marker is developed. The present application realizes accurate evaluation of highland barley germplasm and accurate positioning of major effective genes, and the phenotype detection is efficient and lossless, the molecular marker selection is accurate, and the present application can also be applied to other cereal crops.

The invention discloses a heart failure key gene identification method and system based on a spline regression model and overall entropy change. The method comprises the following steps of: 1, preprocessing data; 2, analyzing time sequence differential expression; 3, constructing a dynamic co-expression network; 4, network overall entropy change analysis; and 5, screening the heart failure key gene. Compared with an existing analysis scheme in which time sequence differential expression analysis and network topology analysis are mutually independent, a coherent technical system from'capturing gene expression time sequence trend 'to'quantifying gene network overall structure evolution' and then'screening key genes' is constructed. The thought jumps out of the limitation of a single method dimension, and more fitting and more systematic analysis of the continuous nonlinear evolution process of the heart failure are realized. Meanwhile, the spline regression model is matched with dynamic network overall entropy change analysis, so that the synergistic effect of effectively screening and comprehensively analyzing differential genes is achieved.

The invention discloses a targeted nutrient source mining method based on beef cattle intestinal type-host gene interaction, particularly relates to the field of biological information data processing, and is used for solving the problems that existing nutrient source development lacks molecular mechanism verification and is poor in targeting. The method comprises the following steps: firstly, dividing nutrition-associated intestinal types based on microbiome and transcriptome data, and constructing a co-expression network to screen host co-expression specific genes driven by the specific intestinal types; then, establishing a flora metabolite ligand set and host protein receptor model, executing molecular conformation search and Gibbs free energy calculation, and screening high-activity targeting effect factors based on physical affinity; and finally, traversal matching and quantitative screening are carried out on the natural raw material liquid chromatography-mass spectrometry data by using the targeted effect factors, and a targeted nutrition source recommendation list aiming at the specific intestine type is generated. According to the method, multi-omics correlation analysis and molecular thermodynamics verification are fused, and an accurate development closed loop from micromechanism analysis to macroscopic raw material matching is constructed.

The application discloses a method for constructing a gene regulation network based on meta-analysis, and aims to solve the problem that a gene regulation network has a large error and a low accuracy because different research data are not completely homogeneous when the gene regulation network is expanded by combining data of multiple studies to enlarge a sample size, and the method comprises the following steps: performing meta-analysis on each transcriptomic gene expression dataset obtained to obtain a significant differential gene list; obtaining a transcription factor gene list according to a species to which the transcriptomic gene expression data belong, and generating a core gene list according to the transcription factor gene list and the significant differential gene; calculating a Pearson correlation coefficient of each core gene and each significant differential gene, and constructing a co-expression network according to the Pearson correlation coefficient; clustering the co-expression network to obtain a plurality of differential gene modules with high correlation of the core genes; and combining biological knowledge and a structural equation model to construct a corresponding gene regulation network according to each module. The application belongs to the field of gene regulation networks.

The invention relates to the technical field of gene engineering detection, and particularly discloses a gene detection system for promoting andrographolide synthesis based on a biosensor, a key gene ApDof29 for regulating and controlling andrographolide synthesis is determined through whole genome analysis, gene expression profile research and co-expression network analysis, the core regulation and control effect of ApDof29 is verified, and the gene detection system for promoting andrographolide synthesis based on the biosensor is used for promoting andrographolide synthesis. The invention also develops a real-time dynamic monitoring system, and realizes non-invasive monitoring of andrographolide content accumulation, biosynthesis rate and metabolic intermediate concentration change by utilizing the fusion of a specific response element and a reporter gene. Through comprehensive calculation processing of an accumulation abnormal coefficient, a synthesis rate abnormal coefficient and a concentration change abnormal coefficient, the regulation and control effect of ApDof29 is dynamically evaluated, and a scientific basis is provided for subsequent gene modification.

The application provides a serum proteomics and machine learning-based Kawasaki disease intravenous immunoglobulin (IVIG) resistance biomarker combination and screening method, and belongs to the technical field of medicines. The method comprises the following steps: (1) on the basis of establishing strict inclusion criteria and typing criteria, collecting whole blood samples of IVIG reaction type and non-reaction type Kawasaki disease children before treatment; (2) using DIA proteomics technology for systematic screening and differential protein identification; (3) weighted co-expression network analysis, screening of protein modules significantly related to IVIG non-reaction phenotype; (4) combined with LASSO-Logistic regression and SVM-RFE for multi-step feature screening, identifying five biological markers significantly related to IVIG resistance: PLA2G4A, SNX17, PURB, CERS3 and CASP1 (5) based on the marker expression level, using the pROC package for ROC analysis and calculating AUC; (6) analyzing the correlation between the marker and the clinical index related to Kawasaki disease; (7) after limma processing in the independent transcriptome set GSE18606, using glm to construct a multivariate binary logistic regression prediction model and perform ROC analysis. Through independent transcriptome dataset verification, the biomarker combination screened by the application can realize effective prediction of Kawasaki disease IVIG resistance, and shows good prediction performance and clinical application value.

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 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 belongs to the technical field of biological information data processing, and particularly relates to a spatial gene co-expression network construction system based on in-situ sequencing. First, in-situ sequencing raw data of a target tissue section is acquired, the raw pixel coordinates are linearly scaled and translationally corrected into objective and true raw spatial physical coordinates, and a gene expression matrix is statistically generated; second, the real spatial distance between sequencing sites is calculated based on the raw spatial physical coordinates, and a spatial adjacency graph is constructed in combination with a dynamic distance threshold and a local neighborhood constraint algorithm; and finally, the gene expression matrix is mapped into the spatial adjacency graph to extract local spatial co-expression features and construct a network. The application eliminates physical scale distortion caused by underlying hardware sampling, flexibly adapts to the cell density and heterogeneity characteristics of different tissues, effectively avoids loss of real biological communication links in sparse areas, and realizes high-fidelity reconstruction of gene synergistic regulation networks in microenvironments.

The invention discloses a cotton ABA response abiotic stress-based core gene rapid screening method, and relates to the field of agricultural biology, and the method comprises the following steps: S1, carrying out exogenous abscisic acid ABA pretreatment on cotton seedlings; s2, applying at least one kind of abiotic stress treatment to the pretreated cotton seedlings; s3, performing transcriptome sequencing and plant hormone content determination to obtain a gene expression profile and hormone content data; s4, screening differential expression genes which are subjected to co-response under the ABA pretreatment and the abiotic stress treatment; s5, constructing a co-expression network and identifying a key regulation and control module related to ABA response; s6, screening candidate core genes from the key regulation and control module; according to the method, through integration of ABA preprocessing, multi-stress simulation, multi-omics data and network analysis, rapid and accurate screening of core stress-resistant genes is realized.

The application discloses a targeted nutrient source mining method based on beef cattle intestinal type-host gene interaction, and particularly relates to the field of biological information data processing, and is used for solving the problems of lack of molecular mechanism verification and poor targeting of existing nutrient source development. First, based on the microbiome and transcriptome data, the nutrient-related intestinal type is divided, and the co-expression network is constructed to screen the host co-expression specific genes driven by the specific intestinal type. Then, the model of the group metabolite ligand set and the host protein receptor is established, the molecular conformation search and the Gibbs free energy calculation are performed, and the high-activity targeted effect factor is screened based on the physical affinity. Finally, the targeted effect factor is used for traversing matching and quantitative screening of natural raw material liquid chromatography-mass spectrometry data, and a targeted nutrient source recommendation list for the specific intestinal type is generated. The application combines multi-omics correlation analysis and molecular thermodynamic verification, and constructs a precise development closed loop from micro mechanism analysis to macro raw material matching.

The present application relates to a kind of corn nitrogen response gene regulatory element analysis method.By the fusion application of transcriptome sequencing, gene co-expression network construction, gene function annotation, element enrichment analysis and yeast one-hybrid technology, the complex gene regulatory network is simplified to excavate the key transcriptional regulatory factor therein.The regulatory element analysis and transcription factor mining using the method of the present application can reduce the analysis difficulty and improve the accuracy of prediction.

The invention discloses application of a potato StHSF1 gene in alkaloid biosynthesis regulation and control, and belongs to the technical field of biology. According to the invention, potatoes with different pulp colors are studied, 48 alkaloids are identified and the accumulation dynamics of the alkaloids are determined, and the colored pulp potato clone is found to have richer alkaloid types and higher alkaloid content than a yellow pulp clone. A key structural gene is screened by systematically analyzing synthesis pathways of various alkaloids, transcription factors possibly participating in regulation are mined based on a co-expression network, and the StHSF1 gene is identified as a potential core hub regulation factor. Functional verification experiments show that transient overexpression of StHSF1 can significantly increase the total alkaloid content and specifically up-regulate the expression of 15 alkaloids. The invention provides a key genetic resource and a theoretical basis for cultivating a new potato variety with optimized alkaloid components, and is beneficial for increasing the nutritional value or reducing toxic alkaloid to guarantee the edible safety and the like by enriching functional alkaloid.