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

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...

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Autores principales: Sam, Lee T, Mendonça, Eneida A, Li, Jianrong, Blake, Judith, Friedman, Carol, Lussier, Yves A
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2646241/
https://www.ncbi.nlm.nih.gov/pubmed/19208196
http://dx.doi.org/10.1186/1471-2105-10-S2-S8
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author Sam, Lee T
Mendonça, Eneida A
Li, Jianrong
Blake, Judith
Friedman, Carol
Lussier, Yves A
author_facet Sam, Lee T
Mendonça, Eneida A
Li, Jianrong
Blake, Judith
Friedman, Carol
Lussier, Yves A
author_sort Sam, Lee T
collection PubMed
description 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 .
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spelling pubmed-26462412009-02-23 PhenoGO: an integrated resource for the multiscale mining of clinical and biological data Sam, Lee T Mendonça, Eneida A Li, Jianrong Blake, Judith Friedman, Carol Lussier, Yves A BMC Bioinformatics Proceedings 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 . BioMed Central 2009-02-05 /pmc/articles/PMC2646241/ /pubmed/19208196 http://dx.doi.org/10.1186/1471-2105-10-S2-S8 Text en Copyright © 2009 Sam et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Proceedings
Sam, Lee T
Mendonça, Eneida A
Li, Jianrong
Blake, Judith
Friedman, Carol
Lussier, Yves A
PhenoGO: an integrated resource for the multiscale mining of clinical and biological data
title PhenoGO: an integrated resource for the multiscale mining of clinical and biological data
title_full PhenoGO: an integrated resource for the multiscale mining of clinical and biological data
title_fullStr PhenoGO: an integrated resource for the multiscale mining of clinical and biological data
title_full_unstemmed PhenoGO: an integrated resource for the multiscale mining of clinical and biological data
title_short PhenoGO: an integrated resource for the multiscale mining of clinical and biological data
title_sort phenogo: an integrated resource for the multiscale mining of clinical and biological data
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2646241/
https://www.ncbi.nlm.nih.gov/pubmed/19208196
http://dx.doi.org/10.1186/1471-2105-10-S2-S8
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