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GeneNetwork is a combined database and open-source bioinformatics data analysis software resource for systems genetics. [1] This resource is used to study gene regulatory networks that link DNA sequence differences to corresponding differences in gene and protein expression and to variation in traits such as health and disease risk.
Gene Disease Database; Gene Expression Omnibus (GEO [9]): a public functional genomics data repository from the U.S. National Cancer Institute (NCI), which supports array- and sequence-based data. Tools for querying and downloading gene expression profiles are provided.
Structure of a gene regulatory network Control process of a gene regulatory network. A gene (or genetic) regulatory network (GRN) is a collection of molecular regulators that interact with each other and with other substances in the cell to govern the gene expression levels of mRNA and proteins which, in turn, determine the function of the cell.
Initially, the GeneCards database had two main features: delivery of integrated biomedical information for a gene in ‘card’ format, and a text-based search engine. Since 1998, the database has integrated more data resources and data types, such as protein expression and gene network information.
The literature-derived human gene-disease network (LHGDN) is a text mining derived database with focus on extracting and classifying gene-disease associations with respect to several biomolecular conditions. It uses a machine learning based algorithm to extract semantic gene-disease relations from a textual source of interest.
KEGG (Kyoto Encyclopedia of Genes and Genomes) is a collection of databases dealing with genomes, biological pathways, diseases, drugs, and chemical substances.KEGG is utilized for bioinformatics research and education, including data analysis in genomics, metagenomics, metabolomics and other omics studies, modeling and simulation in systems biology, and translational research in drug development.
The DisGeNET database integrates over 400 000 associations between > 17 000 genes and > 14 000 diseases from human to animal model expert curated databases with text mined GDAs from MEDLINE using a NLP-based approach. [4] The highlights of DisGeNET are the data integration, standardisation and a fine-grained tracking of the provenance information.
A gene co-expression network constructed from a microarray dataset containing gene expression profiles of 7221 genes for 18 gastric cancer patients A gene co-expression network (GCN) is an undirected graph , where each node corresponds to a gene , and a pair of nodes is connected with an edge if there is a significant co-expression relationship ...