30 results about "Gene interaction network" patented technology

Detailed information of each analysis subject partial network is displayed on a left pane of a screen. The detailed information includes the number of nodes, the number of edges, and accumulative coverage of the analysis subject partial network for each disease. Based on the detailed information, a user can designate the analysis subject partial network of a disease the user wishes to analyze. When the user has designated the disease, a network diagram indicating a partial network related to the designated disease is displayed on a right pane.

Embodiments of the invention relate to a constructing a gene interaction network. Tools are provided to compute a gene relationship measure based upon cellular images, and to rank image collections having a similar morphology. The ranking is based upon capturing similarity within the ranked collection by modeling a three dimensional shape of a cellular image stack. The graph is constructed for related images stacks. Nodes in the graph represent genes, and edges drawn between the nodes represent corresponding image stacks in a commonly ranked list. Accordingly, the graphical representation mathematically and visually connects respective genes.

Embodiments of the invention relate to a constructing a gene interaction network. Tools are provided to compute a gene relationship measure based upon cellular images, and to rank image collections having a similar morphology. The ranking is based upon capturing similarity within the ranked collection by modeling a three dimensional shape of a cellular image stack. The graph is constructed for related images stacks. Nodes in the graph represent genes, and edges drawn between the nodes represent corresponding image stacks in a commonly ranked list. Accordingly, the graphical representation mathematically and visually connects respective genes.

The invention belongs to the technical field of bioinformatics, and relates to a method for identifying esophageal squamous cell carcinoma markers on the basis of network index difference analysis. The method comprises the following steps of processing esophageal squamous cell carcinoma gene sample data and normal gene sample data to construct an esophageal squamous cell carcinoma gene interactionnetwork and a normal gene interaction network; using a network module identification method to find out key community structures in the two networks, and performing gene function enrichment analysison the key community structures; extracting same nodes from the two networks, and retaining nodes linked to the same nodes to obtain the two simplified networks; using a global index and a local modular index, analyzing the two simplified networks to obtain genes related to esophageal squamous cell carcinoma; combining a gene function enrichment analysis result with gene annotations and functionalreferences to finally determine candidate markers for diagnosis of esophageal squamous cell carcinoma. A method for studying the esophageal squamous cell carcinoma markers on the basis of gene network difference analysis is further perfected.

The invention discloses a functional peptide screening method which is low in cost, efficient and environment-friendly and specifically comprises the following steps: calling a regulatory network related to the function type of a functional peptide needing to be screened; finding out a first gene target set related to the function type; performing functional target prediction on each sequenced peptide fragment, and screening to obtain a second gene target set related to the functional type; performing association processing on the second gene target set and the first gene target set to obtain a functional target; constructing a protein interaction network according to the functional targets, carrying out node graph visualization analysis to obtain a gene interaction network graph, and identifying to obtain a core module and a key gene; calculating node edge numbers of proteins and genes by using an R language, and performing normalization processing to obtain a first score of each target in the functional targets; multiplying the first score of each target point of the single peptide fragment by a corresponding similarity coefficient and then summing to obtain a functional score of the single peptide; and selecting the first 20 peptide fragments with hydrophobicity of 0-3 as candidate peptides.

The invention discloses a plant salt-tolerant gene module mining method. The method comprises the following steps that: based on a gene interaction network, a Markov clustering algorithm is adopted to carry out modular analysis on the gene interaction network, and gene chip and whole genome associated data are utilized to evaluate the salt tolerances of gene modules; and a key salt-tolerant gene module is screened out according to the salt-tolerant contribution of each gene module. Compared with an existing method, the method focuses on discovery of a brand new salt-tolerant gene group, and greatly improve the efficiency and accuracy of plant salt-tolerant gene mining.

The invention discloses a gene selection method and system based on a self-adaptive gene interaction regularization elastic network model. The method comprises the following steps: checking and evaluating the importance degree of each measured gene based on a Wilcoxon rank sum; quantifying the importance degree of each measured gene, adding an adaptive penalty weight, and then deleting noise genes to obtain feature genes; introducing the penalty weight into a least square loss function, and constructing a self-adaptive elastic network model; constructing an adjacent matrix of the gene interaction network; constructing a gene interaction network penalty based on the adjacent matrix; combining the self-adaptive elastic network model with gene interaction network penalty to construct a self-adaptive gene interaction regularization elastic network model; solving an optimal solution of the regularization elastic network model based on a gradient descent algorithm, and selecting a gene based on the optimal solution. According to the method, important genes highly related to tumor generation can be adaptively selected, and redundant and unrelated genes and noise genes are removed.

The invention relates to the field of data mining in bioinformatics, in particular to a synthetic lethal interaction prediction method based on a heterogeneous graph convolutional neural network. The method mainly comprises the steps of (1) collecting breast cancer FPKMRNA-seq information, breast cancer CNV data and breast cancer high-confidence risk gene information; (2) preprocessing breast cancer copy number variation (CNV) and mRNA data by a rank-based method; (3) establishing a gene-gene interaction network based on protein interaction knowledge and a signal channel, and generating a high-density sub-network by using a network clustering algorithm; and (4) constructing a group sparse learning-based model to describe the incidence relation between the subnet and the mRNA expression of the target gene, measuring the ability of the CNVs to predict the expression change of the target gene by using a root mean square error (RMSE), carrying out cross validation by adopting a k-fold cross validation algorithm, and carrying out correlation analysis on the gene expression and the CNV by using a Sperman correlation research method in combination with pathway enrichment analysis.