Extraction of Conditional Probabilities of the Relationships Between Drugs, Diseases, and Genes from PubMed Guided by Relationships in PharmGKB

Guided by curated associations between genes, treatments (i.e., drugs), and diseases in pharmGKB, we constructed n-way Bayesian networks based on conditional probability tables (cpt’s) extracted from co-occurrence statistics over the entire Pubmed corpus, producing a broad-coverage analysis of the r...

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Detalles Bibliográficos
Autores principales: Theobald, Martin, Shah, Nigam, Shrager, Jeff
Formato: Texto
Lenguaje:English
Publicado: American Medical Informatics Association 2009
Materias:
Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041559/
https://www.ncbi.nlm.nih.gov/pubmed/21347183
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author Theobald, Martin
Shah, Nigam
Shrager, Jeff
author_facet Theobald, Martin
Shah, Nigam
Shrager, Jeff
author_sort Theobald, Martin
collection PubMed
description Guided by curated associations between genes, treatments (i.e., drugs), and diseases in pharmGKB, we constructed n-way Bayesian networks based on conditional probability tables (cpt’s) extracted from co-occurrence statistics over the entire Pubmed corpus, producing a broad-coverage analysis of the relationships between these biological entities. The networks suggest hypotheses regarding drug mechanisms, treatment biomarkers, and/or potential markers of genetic disease. The cpt’s enable Trio, an inferential database, to query indirect (inferred) relationships via an SQL-like query language.
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spelling pubmed-30415592011-02-23 Extraction of Conditional Probabilities of the Relationships Between Drugs, Diseases, and Genes from PubMed Guided by Relationships in PharmGKB Theobald, Martin Shah, Nigam Shrager, Jeff Summit on Translat Bioinforma Articles Guided by curated associations between genes, treatments (i.e., drugs), and diseases in pharmGKB, we constructed n-way Bayesian networks based on conditional probability tables (cpt’s) extracted from co-occurrence statistics over the entire Pubmed corpus, producing a broad-coverage analysis of the relationships between these biological entities. The networks suggest hypotheses regarding drug mechanisms, treatment biomarkers, and/or potential markers of genetic disease. The cpt’s enable Trio, an inferential database, to query indirect (inferred) relationships via an SQL-like query language. American Medical Informatics Association 2009-03-01 /pmc/articles/PMC3041559/ /pubmed/21347183 Text en ©2009 AMIA - All rights reserved. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose
spellingShingle Articles
Theobald, Martin
Shah, Nigam
Shrager, Jeff
Extraction of Conditional Probabilities of the Relationships Between Drugs, Diseases, and Genes from PubMed Guided by Relationships in PharmGKB
title Extraction of Conditional Probabilities of the Relationships Between Drugs, Diseases, and Genes from PubMed Guided by Relationships in PharmGKB
title_full Extraction of Conditional Probabilities of the Relationships Between Drugs, Diseases, and Genes from PubMed Guided by Relationships in PharmGKB
title_fullStr Extraction of Conditional Probabilities of the Relationships Between Drugs, Diseases, and Genes from PubMed Guided by Relationships in PharmGKB
title_full_unstemmed Extraction of Conditional Probabilities of the Relationships Between Drugs, Diseases, and Genes from PubMed Guided by Relationships in PharmGKB
title_short Extraction of Conditional Probabilities of the Relationships Between Drugs, Diseases, and Genes from PubMed Guided by Relationships in PharmGKB
title_sort extraction of conditional probabilities of the relationships between drugs, diseases, and genes from pubmed guided by relationships in pharmgkb
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041559/
https://www.ncbi.nlm.nih.gov/pubmed/21347183
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