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 method and system for screening cancer biomolecular markers based on network topology parameters. The method includes: acquiring human intergenic interaction network and gene chip expression data, and integrating the intergenic interaction based on the gene expression data. Network; construct the interaction network between genes in the disease state and the control state; calculate the network topology parameter difference gene between the disease state and the control state gene interaction network, and obtain the network topology parameter difference change network based on the network parameter difference gene; Mining the network modules of the topological parameter difference network; performing feature selection on the obtained difference network modules to obtain the genes that are distinguishable between normal and disease in each module; detecting the classification effect of the genes selected in each module on diseases, and screening according to the classification effect Differential network modules as biomolecular marker candidates. The present invention provides a new complex disease biomarker recognition method based on omics data, which is proved to have certain accuracy and effectiveness by experiments.

The present invention provides a method for identifying heterogeneous cancer driver genes based on a mutually exclusive constrained graph Laplacian. First, obtain cancer genome variation data and gene interaction network; then, use a matrix model to describe the heterogeneity of cancer, and use mutually exclusive constraint matrix decomposition to estimate the sample parameters of heterogeneity cancer; Then, construct the mutual exclusion constraint matrix decomposition optimization function regularized by the joint association and interaction network, and correct the parameters of the driving genes affected by the interaction in the local samples through iterative solution; finally, use the outlier test method to identify the driving genes . The invention can solve the problem of differential estimation of parameters of cancer samples and effective identification of driver genes in local samples affected by interaction, and realizes the identification of driver genes mutated in local samples from gene variation data of heterogeneous cancer samples.

The invention provides a method for screening potentially important genes of a population genome based on MK-test, which belongs to the technical field of gene screening, including: 1) constructing a gene interaction network; 2) using a processing module to obtain and / or input gene parameters as required; 3) After the gene parameters are calculated by a standard formula, the inter-species and / or intra-species variation rates are output, and the potentially important genes of the population genome are screened out; 4) the functional analysis of the potentially important genes is carried out; wherein the processing module includes at least two A gene chip, a computer connected to a gene chip. The present invention can simultaneously analyze the differences between species or within species in large batches, and can effectively analyze the trend of the ratio of non-expressed segments and / or the ratio of expressed segments with changes in genes after calculation, providing a basis for screening potentially important genes in population genomes an efficient path.

The invention relates to an epigenome-based drug discovery method and application thereof, belonging to the technical field of biomedicine. Starting from the basic principle of drug action, the present invention combines the DNA methylation chip data of patient samples and the interaction network between genes, and uses the revised PageRank algorithm to mine the key pathogenic genes in the process of disease pathogenesis, so that according to the corresponding relationship of drug targets , to search for potential drugs to treat diseases. This method has the characteristics of solid theoretical foundation, easy implementation and high efficiency, and has broad application prospects in drug discovery.

The invention discloses a gene selection method and system based on an adaptive gene interaction regularization elastic network model. The method includes: evaluating the importance of each measured gene based on the Wilcoxon rank sum test; Quantify and add adaptive penalty weights, and then delete noise genes to obtain eigengenes; introduce penalty weights into the least squares loss function to construct an adaptive elastic network model; construct the adjacency matrix of gene interaction network; construct genes based on adjacency matrix Interactive network penalty; combine the adaptive elastic network model and gene interaction network penalty to construct an adaptive gene interaction regularized elastic network model; solve the optimal solution of the regularized elastic network model based on the gradient descent algorithm, based on the optimal solution Select genes. The present invention can adaptively select important genes that are highly correlated with the generation of tumors, and remove redundant, irrelevant genes and noise genes.

The invention discloses a method for rapidly constructing a Cas9 binary expression vector library paired with sgRNA, and relates to a method for rapidly constructing a binary vector in the field of genetic engineering. In this method, on the basis of constructing the CRISPR-Cas9 binary expression vector, the corresponding forward and reverse primers are designed according to the specific gene design target sequence, and the primer has two BsaI restriction sites; the target sequence and two BsaI sites are obtained by one-step PCR The target fragment, and then construct the above fragment into a binary expression vector with Cas9 gene by simple digestion and ligation to form a paired sgRNA Cas9 vector. The invention can efficiently establish random libraries and non-random libraries, which can be used for large-scale screening of gene functions in plants, and can be used for the construction and research of gene interaction networks between multiple genes; the experimental results prove that this method successfully and efficiently constructs Cas9 binary expression vector library of random paired sgRNAs for 14 targets (from 14 genes).

A method for mining drought-resistant gene modules in plants. The method is based on the gene interaction network, and uses the Markov clustering algorithm to conduct modular analysis on the gene interaction network, and uses gene chip and genome-wide association data to determine the drought-resistance effect of gene modules. to evaluate. According to the drought resistance contribution of each gene module, the key drought resistance gene modules were screened. Compared with existing methods, this method focuses on the discovery of new drought-resistant gene groups, which greatly improves the efficiency and accuracy of plant drought-resistant gene discovery.

Embodiments of the invention relate to a method, system, and computer program product to construct a gene interaction network by combining two sources of genomic information, namely RNAi imaging data and gene expression data. Tools are provided to gather data, including gene expression data and gene image data, and to compute measurements and relationships, respectively. A graph is constructed with nodes representing genes and edges drawn between the nodes to form gene clusters. The graph is refined such that the shape captures a structural pattern of the cluster.