DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes

By Janet Piñero González, Núria Queralt-Rosinach, À. Bravo, Jordi Deu-Pons, A. Bauer-Mehren, M. Baron, Ferran Sanz, Laura Inés Furlong. Database J. Biol. Databases Curation, 2015

1,063 citations recorded by the source corpus.

DisGeNET is a comprehensive discovery platform designed to address a variety of questions concerning the genetic underpinning of human diseases. DisGeNET contains over 380 000 associations between >16 000 genes and 13 000 diseases, which makes it one of the largest repositories currently available of its kind. DisGeNET integrates expert-curated databases with text-mined data, covers information on Mendelian and complex diseases, and includes data from animal disease models. It features a score based on the supporting evidence to prioritize gene-disease associations. It is an open access resource available through a web interface, a Cytoscape plugin and as a Semantic Web resource. The web interface supports user-friendly data exploration and navigation. DisGeNET data can also be analysed via the DisGeNET Cytoscape plugin, and enriched with the annotations of other plugins of this popular network analysis software suite. Finally, the information contained in DisGeNET can be expanded and complemented using Semantic Web technologies and linked to a variety of resources already present in the Linked Data cloud. Hence, DisGeNET offers one of the most comprehensive collections of human gene-disease associations and a valuable set of tools for investigating the molecular mechanisms underlying diseases of genetic origin, designed to fulfill the needs of different user profiles, including bioinformaticians, biologists and health-care practitioners. Database URL: http://www.disgenet.org/

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/666fbfd9410a97f42686beec53be5e1d58212937

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Citations in the corpus

Integrating biomedical research and electronic health records to create knowledge-based biologically meaningful machine-readable embeddings (2019, methodology): “In SPOKE, the ASSOCIATES_- DaG edges represent known associations between Diseases and Genes and were obtained from the GWAS Catalog 15 , DISEASES 16 , DisGeNET 17,18 , and DOAF 19 .”
The DisGeNET knowledge platform for disease genomics: 2019 update (2019, background): “Originally implemented in 2010 as a Cytoscape plugin (6), during the last years DisGeNET has evolved into different formats and tools (7–10), and it now undergoes its sixth release as a knowledge management platform aimed at supporting different application scenarios and users.”
Embedding Logical Queries on Knowledge Graphs (2018, methodology): “We obtained relationships between proteins and diseases from the DisGeNET database [31], which integrates data from expert-curated repositories.”
HIPPIE v2.0: enhancing meaningfulness and reliability of protein–protein interaction networks (2016, background): “Many studies use these PPI networks to identify commonalities among a group of proteins (3), to infer novel functions for a gene of interest (4) or for gene-disease association prediction (5,6).”
Systematic integration of biomedical knowledge prioritizes drugs for repurposing (2017, background): “DisGeNET integrates evidence from over 10 sources and reports a single score for each association ( Pin˜ero et al., 2015 ; Pin˜ero et al., 2017 ).”
WikiPathways: capturing the full diversity of pathway knowledge (2015, methodology): “Integrated into the semantic web as unified resource identifiers (URIs), our content is being used to link pathways to concepts such as disease genes curated by Dis-Nucleic GeNET (21).”
NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis (2019, methodology): “Additionally, we have included gene-disease association networks for humans from DisGeNet (27) which is a comprehensive database covering most of the known human disease-specific genotype– phenotype relationships.”
MalaCards: an amalgamated human disease compendium with diverse clinical and genetic annotation and structured search (2016, background): “We subjected MalaCards to an advanced search on 1000 randomly-selected disease names from DisGeNET (5).”