DisGeNET: Difference between revisions

Revision as of 09:02, 16 August 2015

Revision as of 09:05, 16 August 2015

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==Integrated data==

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.<ref name="befreeBravo">{{cite journal|last1=Bravo|first1=A|last2=Piñero|first2=J|last3=Queralt-Rosinach|first3=N|last4=Rautschka|first4=M|last5=Furlong|first5=LI|title=Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research.|journal=BMC Bioinformatics|date=21 February 2015|volume=16|issue=55|pages=|doi=10.1186/s12859-015-0472-9}</ref> The highlights of DisGeNET are the data integration, standardisation and a fine-grained tracking of the provenance information. The integration is performed by means of gene and disease vocabulary mapping and by using the DisGeNET association type ontology. Furthermore, GDAs are organised according to their type and level of evidence as CURATED, PREDICTED and LITERATURE, and they are also scored based on the supporting evidence to prioritise and ease their exploration.

==The DisGeNET Association Type Ontology==

For a seamless integration of gene-disease association data, we developed the DisGeNET association type ontology. All association types as found in the original source databases are formally structured from a parent GeneDiseaseAssociation class if there is a relationship between the gene/protein and the disease, and represented as ontological classes. It is an OWL ontology that is integrated into the [http://code.google.com/p/semanticscience/wiki/SIO Sematicscience Integrated Ontology (SIO)], which provides essential types and relations for the rich description of objects, processes and their attributes.<ref name="Dumontier">{{cite journal|last1=Dumontier|first1=Michel|last2=Baker|first2=Christopher JO|last3=Baran|first3=Joachim|last4=Callahan|first4=Alison|last5=Chepelev|first5=Leonid|last6=Cruz-Toledo|first6=José|last7=Del Rio|first7=Nicholas R|last8=Duck|first8=Geraint|last9=Furlong|first9=Laura I|last10=Keath|first10=Nichealla|last11=Klassen|first11=Dana|last12=McCusker|first12=James P|last13=Queralt-Rosinach|first13=Núria|last14=Samwald|first14=Matthias|last15=Villanueva-Rosales|first15=Natalia|last16=Wilkinson|first16=Mark D|last17=Hoehndorf|first17=Robert|title=The Semanticscience Integrated Ontology (SIO) for biomedical research and knowledge discovery|journal=Journal of Biomedical Semantics|date=2014|volume=5|issue=1|pages=14|doi=10.1186/2041-1480-5-14}}</ref> You can check SIO gene-disease association classes from this [http://semanticscience.org/resource/SIO_000983 URL].

==Cytoscape plugin==