30 results about "Coexpression network" patented technology

The invention discloses a gene expression time series data classification method based on a visibility graph algorithm. The method comprises the steps of (1) constructing a basic network, selecting adata strip according to preprocessed gene expression time series data, constructing a visibility graph and a connection graph by using the visibility graph algorithm, and determining a basic structureof a co-expression network, (2) extracting relevant traditional features according to the obtained basic network, (3) obtaining a feature vector of each gene node in the basic network by using second-order random walk and neural network model learning, and (4) integrating features of the basic network and using different strategies based on the obtained features of the basic network through a density clustering algorithm to complete the classification of gene expression time series data. The invention provides a method for realizing the gene expression time series data classification by usingvisibility graph foundation network construction, node feature vector extraction and the density clustering algorithm, and the method has good precision and practical performance.

The invention belongs to the field of molecular breeding, and mainly relates to a method for screening quantitative character candidate genes. The method includes the steps: extracting genes of quantitative character related intervals (QTL interval and GWAS interval) as candidate gene sets; collecting known genes related to the characters as bait genes; carrying out gene co-expression analysis orweight co-expression analysis on the transcriptome data of a set of samples; constructing a co-expression network; and selecting genes with strong connectivity to the bait genes as candidate genes. Compared with previous analysis methods, the method for screening quantitative character candidate genes can quickly and effectively screen candidate genes in the relevant range of quantitative characters, can narrow the screening range of candidate genes, can predict the interaction between genes, can overcome the defect that traditional fine positioning is large in workload and cumbersome, and canaccelerate the molecular breeding process of species.

The invention provides a comprehensive risk scoring method for multiple myeloma, comprising the following steps: S1, obtaining a gene expression profile GSE24080 of an MM patient from a GEO database,and pre-treating genes in the gene expression profile GSE24080 to obtain the first 25% of the 5413 genes with the largest variance of expression value; S2, subjecting the 5413 genes to WGCNA gene co-expression network analysis to identify co-expressed functional modules; S3, evaluating the correlation between the functional modules and clinical information through Pearson correlation test to determine the most significant modules; S4, performing univariate survival analysis on the genes in the most significant modules by using a Cox proportional hazard model, and screening out a ten-gene scoring model composed of the 10 best genes through LASSO regression; and S5, setting the rule that each factor scores 1 point when the ten-gene scoring model or serum [beta]2M or LDH is higher than a cut-off value and otherwise 0, and establishing a comprehensive risk scoring system.

The invention discloses a construction method and system of a heuristic metabolic co-expression network. According to the most dependent criterion that mutual information of multiple metabolite features serves as a fitness function value, a heuristic intelligent multimode optimization algorithm is used to search optimization from an optimal feature subset. The optimization process is carried out for multiple times, and results of the multiple times are combined and learned to construct the co-expression network structure. A probability model is used to calculate a segmentation threshold, and the accurate and stable metabolic co-expression network is obtained.

The invention belongs to the field of plant molecular biotechnology and genetic engineering and relates to a method for predicting unknown corn gene functions, in particular to a method for predictingunknown corn gene functions on the basis of oil content-related genes and oil content dynamic correlation. The method comprises the following steps: firstly, collecting the grain transcript sequencing 15 days after pollination of a corn selfing line to obtain gene expression quantity data; collecting gene data associated with the oil content of the corn kernels; collecting grain oil content dataof an associated group formed by the corn selfing line; establishing a dynamic association analysis LA model; carrying out LA significance evaluation; excavating a gene for regulating and controllingthe dynamic correlation between the oil content associated gene and the oil content within the whole genome range; and performing function annotation on the gene with the obvious LA result, and predicting the function of an unknown gene. According to the method, the phenomenon that genes in corn kernels are dynamically associated with a co-expression mode is taken as a breakthrough. The function of an unknown gene is predicted. Compared with traditional co-expression network construction, the regulation gene for regulating the co-expression mode can be quickly found through dynamic correlationanalysis.

The invention belongs to the field of plant molecular biotechnology and gene engineering and particularly relates to a method for predicting functions of unknown genes of maize based on dynamic correlation between gene expression level and characters. The method comprises the following steps: firstly collecting grain transcripts obtained after pollinating maize inbred lines for 15 days, and sequencing to obtain gene expression level data; building a dynamic correlation analysis LA model; evaluating significance of LA; excavating dynamic correlation of gene expression patterns of whole genomes of the maize; and carrying out function annotation of genes of significant LA results and predicting functions of the genes. The method is capable of predicting the functions of the unknown genes by taking the phenomenon of dynamic correlation between co-expression patterns of gene pairs in the maize grains as sally port; compared with the conventional co-expression network construction, the dynamic correlation analysis is capable of rapidly finding regulation genes for regulating the co-expression patterns.

The present application provides a method, device, equipment and storage medium for constructing a tumor gene co-expression network. The method includes: acquiring the cancerous transcriptome sequencing data of various tissues in the target human body system and the normal transcriptome sequencing data of the various tissues; the target human body system is any one of the eight major human body systems of human anatomy ; According to the relationship between genes and transcripts, as well as RNAs with uniform gene expression in normal transcriptome sequencing data, differential mRNAs, lncRNAs and miRNAs are extracted from cancer transcriptome sequencing data of various tissues; Carcinogenic differential mRNA, lncRNA, and miRNA of various tissues were analyzed by GO, and a target expression matrix was obtained; the target expression matrix was analyzed by WGCNA, expression modules were constructed according to traits, and the most relevant modules were selected for network construction. It can realize network construction for various cancers.

The invention provides a corn variable splicing isomer function prediction system based on tissue specificity, which is characterized in that isoform co-expression networks of a plurality of tissues are constructed by processing expression data of isoform on each tissue, and the isoform co-expression networks of each tissue are integrated through self-adaptive weight, so that the function of the corn variable splicing isomer is predicted. A high-quality isoform organization specific association network can be obtained; according to the method, an isoform sequence similarity network is constructed by using isoform sequence data, and the isoform sequence similarity network is fused with an isoform organization specificity association network, so that a better isoform function association network can be obtained; multi-example learning is performed through non-negative matrix factorization, and meanwhile, the non-negative matrix factorization is guided by using an isoform function association network, so that more accurate and more comprehensive function prediction of isoform is realized.

The invention provides a method for constructing a nomogram of multiple myeloma based on ten gene characteristics, serum [beta]2 microglobulin and LDH. The method comprises the following steps: S1, obtaining a gene expression profile GSE24080 of an MM patient from a GEO database, and pre-treating genes in the gene expression profile GSE24080 to obtain the first 25% of the 5413 genes with the largest variance in expression values; S2, subjecting the 5413 genes to WGCNA gene co-expression network analysis to identify function modules of co-expression; S3, evaluating the correlation between the functional modules and clinical information by Pearson correlation test to determine the most significant module; S4, performing univariate survival analysis on the genes in the most significant modules using a Cox proportional hazard model, and screening a score model consisting of 10 best genes by LASSO regression; and S5, establishing a novel nomogram based on the scoring model, serum [beta]2M and high LDH to predict 3-year OS and 5-year OS of newly diagnosed MM patients.

The invention discloses a disease correlation analysis system and method based on gene big data, and relates to the technical field of biological information. Comprising a co-expression network diagram module, a pathogenic SNP group detection module and a disease association model generation module, the co-expression network diagram module obtains gene expression data sets of a plurality of users suffering from target diseases; constructing a co-expression network diagram of the gene expression data set; the pathogenic SNP group detection module determines a pathogenic SNP group of the target disease according to the co-expression network diagram; and the disease association model generation module quantifies the association relationship between the target disease and the pathogenic SNP group to obtain a disease association model of the target disease. The co-expression network diagram is constructed through the gene expression data set, and then the pathogenic SNP group of the target disease is determined, so that the relationship between the target disease and the pathogenic SNP group can be quantified, and the disease relevance between the gene expression and the disease is enhanced.

A cancer target marker mapping method based on a co-expression network, comprising the following steps: 1) constructing a co-expression basic network, and calculating an adjacency matrix and a topology matrix according to gene expression data of characteristic genes; 2) extracting the characteristics of the co-expression basic network , that is to convert each gene node of the topological network into an eigenvector as the eigenvalue of the network; 3) train the neural network model, and train the parameters of the neural network model according to the walk sequence; 4) conduct cancer target marker mapping, according to the An adaptive algorithm for cluster centers of density peaks for automatic discovery of targeted gene communities. The invention provides a method with good universality and precision, which adopts co-expression basic network construction, node feature vector extraction and gene community automatic discovery to realize target gene mapping.

The invention discloses a subgroup-specific co-expression network identification method. The invention provides a method for identifying the specific co-expression network of a subgroup of samples to be tested, comprising the following steps: 1) processing the original UMI number values ​​of each gene in each sample in the subgroup of samples to be tested to be normalized; 2) converting the original UMI Filter the homogenization results, 3) calculate the pairwise weighted median correlation bicor of the filtered genes; 4) filter the weighted median correlation results to obtain strong co-expression gene relationship pairs; 5) filter the cell subgroups to be tested Strongly co-expressed gene relationship pairs, subtracting the shared strong co-expressed gene relationship pairs in all cells in the subgroup, that is, to obtain the specific co-expressed gene pairs of the cell subgroup; and then using the cell subgroup Group-specific co-expressed gene pairs were used to construct a subgroup-specific gene co-expression network of the sample to be tested. The method for subgroups of the present invention is more targeted and more sensitive.

The invention provides a method for identifying key modules of Parkinson's disease based on miRNA sequencing data. First, preprocess the high-throughput sequencing data; then, group the samples according to different stages of PD disease, and then perform differential expression analysis; then, perform hierarchical clustering according to the correlation coefficient between differentially expressed miRNAs to construct co-expression Network and modules; finally, the module network is constructed and the key modules of PD are identified. The method of the invention can identify the key modules of the PD evolution process, judge the PD stage of the current patient according to the key modules, and provide help for doctors to find early PD patients.

The present invention relates to a method for predicting the abnormality of triple-negative breast cancer spindle assembly checkpoints by using lncRNA-mRNA co-expression network, comprising the following steps: experimental grouping, RNA extraction, differential expression analysis of lncRNA chip data, and lncRNA target gene analysis and Control network construction. The method of the present invention using lncRNA-mRNA co-expression network to predict spindle assembly checkpoint abnormality in triple-negative breast cancer provides a new molecular marker for triple-negative breast cancer by predicting spindle assembly checkpoint abnormality through lnRNA-mRNA co-expression network A new approach has been established, which provides a reference for predicting the prognosis of triple-negative breast cancer.

The invention relates to a method for predicting anthracycline drug cardiomyocyte gap junction communication abnormality using miRNA-gene co-expression network, comprising the following steps: Step 1: Experimental grouping; Step 2: RNA extraction; Step 3: miRNA-gene chip data Differential expression analysis; step 4: miRNA target gene analysis and regulatory network construction. The present invention uses the miRNA-gene co-expression network to predict the abnormality of anthracycline drug cardiomyocyte gap junction communication, and predicts the abnormal cardiomyocyte gap junction communication through the miRNA-gene co-expression network, which is a new molecular marker for discovering anthracycline drug cardiotoxicity A new approach is provided, which provides a reference for predicting its prognosis.

A method for identifying coexpressed coding and noncoding genes is disclosed. The method may comprise: receiving a gene sequence; mapping the gene sequence to known coding and non-coding genes; correlating the mapped genes; and generating a co-expression network. A system for generating a co-expression network and providing the co-expression network to a user on a display is disclosed. The system may include memory, one or more processors, one or more databases, and a display.

The invention discloses a method for joint analysis of multi-omics data. The method for joint analysis of multi-omics data provided by the present invention includes the following steps: (A) performing co-expression network analysis on each single-omics index data in the multi-omics data to be analyzed, and finding respective expression modules; B) According to the overlapping relationship between the respective expression modules of different omics data, screen out the significantly correlated interaction modules among the multi-omics data to be analyzed. The multi-omics data joint analysis method provided by the present invention is not limited by the number of omics data, and theoretically can be any number of groups; at the same time, it does not depend on the source of the input data, as long as it is the index data that can measure the corresponding omics ( Such as gene expression value, apparent degree of methylation, SNP mutation rate, etc.), can be used as input data.