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This article is part of the supplement: Selected Proceedings of the First Summit on Translational Bioinformatics 2008

Open Access Proceedings

PhenoGO: an integrated resource for the multiscale mining of clinical and biological data

Lee T Sam14, Eneida A Mendonça1, Jianrong Li1, Judith Blake3, Carol Friedman2* and Yves A Lussier1*

Author Affiliations

1 Center for Biomedical Informatics, Department of Medicine, The University of Chicago, Chicago, IL, USA

2 Department of Biomedical Informatics, Columbia University, New York, NY, USA

3 The Jackson Laboratory, Bar Harbor, ME, USA

4 The University of Michigan, Ann Arbor, MI, USA

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BMC Bioinformatics 2009, 10(Suppl 2):S8  doi:10.1186/1471-2105-10-S2-S8

Published: 5 February 2009

Abstract

The evolving complexity of genome-scale experiments has increasingly centralized the role of a highly computable, accurate, and comprehensive resource spanning multiple biological scales and viewpoints. To provide a resource to meet this need, we have significantly extended the PhenoGO database with gene-disease specific annotations and included an additional ten species. This a computationally-derived resource is primarily intended to provide phenotypic context (cell type, tissue, organ, and disease) for mining existing associations between gene products and GO terms specified in the Gene Ontology Databases Automated natural language processing (BioMedLEE) and computational ontology (PhenOS) methods were used to derive these relationships from the literature, expanding the database with information from ten additional species to include over 600,000 phenotypic contexts spanning eleven species from five GO annotation databases. A comprehensive evaluation evaluating the mappings (n = 300) found precision (positive predictive value) at 85%, and recall (sensitivity) at 76%. Phenotypes are encoded in general purpose ontologies such as Cell Ontology, the Unified Medical Language System, and in specialized ontologies such as the Mouse Anatomy and the Mammalian Phenotype Ontology. A web portal has also been developed, allowing for advanced filtering and querying of the database as well as download of the entire dataset http://www.phenogo.org webcite.