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Development and application of an interaction network ontology for literature mining of vaccine-associated gene-gene interactions

BACKGROUND: Literature mining of gene-gene interactions has been enhanced by ontology-based name classifications. However, in biomedical literature mining, interaction keywords have not been carefully studied and used beyond a collection of keywords. METHODS: In this study, we report the development...

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Autores principales: Hur, Junguk, Özgür, Arzucan, Xiang, Zuoshuang, He, Yongqun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4362819/
https://www.ncbi.nlm.nih.gov/pubmed/25785184
http://dx.doi.org/10.1186/2041-1480-6-2
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author Hur, Junguk
Özgür, Arzucan
Xiang, Zuoshuang
He, Yongqun
author_facet Hur, Junguk
Özgür, Arzucan
Xiang, Zuoshuang
He, Yongqun
author_sort Hur, Junguk
collection PubMed
description BACKGROUND: Literature mining of gene-gene interactions has been enhanced by ontology-based name classifications. However, in biomedical literature mining, interaction keywords have not been carefully studied and used beyond a collection of keywords. METHODS: In this study, we report the development of a new Interaction Network Ontology (INO) that classifies >800 interaction keywords and incorporates interaction terms from the PSI Molecular Interactions (PSI-MI) and Gene Ontology (GO). Using INO-based literature mining results, a modified Fisher’s exact test was established to analyze significantly over- and under-represented enriched gene-gene interaction types within a specific area. Such a strategy was applied to study the vaccine-mediated gene-gene interactions using all PubMed abstracts. The Vaccine Ontology (VO) and INO were used to support the retrieval of vaccine terms and interaction keywords from the literature. RESULTS: INO is aligned with the Basic Formal Ontology (BFO) and imports terms from 10 other existing ontologies. Current INO includes 540 terms. In terms of interaction-related terms, INO imports and aligns PSI-MI and GO interaction terms and includes over 100 newly generated ontology terms with ‘INO_’ prefix. A new annotation property, ‘has literature mining keywords’, was generated to allow the listing of different keywords mapping to the interaction types in INO. Using all PubMed documents published as of 12/31/2013, approximately 266,000 vaccine-associated documents were identified, and a total of 6,116 gene-pairs were associated with at least one INO term. Out of 78 INO interaction terms associated with at least five gene-pairs of the vaccine-associated sub-network, 14 terms were significantly over-represented (i.e., more frequently used) and 17 under-represented based on our modified Fisher’s exact test. These over-represented and under-represented terms share some common top-level terms but are distinct at the bottom levels of the INO hierarchy. The analysis of these interaction types and their associated gene-gene pairs uncovered many scientific insights. CONCLUSIONS: INO provides a novel approach for defining hierarchical interaction types and related keywords for literature mining. The ontology-based literature mining, in combination with an INO-based statistical interaction enrichment test, provides a new platform for efficient mining and analysis of topic-specific gene interaction networks.
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spelling pubmed-43628192015-03-18 Development and application of an interaction network ontology for literature mining of vaccine-associated gene-gene interactions Hur, Junguk Özgür, Arzucan Xiang, Zuoshuang He, Yongqun J Biomed Semantics Research BACKGROUND: Literature mining of gene-gene interactions has been enhanced by ontology-based name classifications. However, in biomedical literature mining, interaction keywords have not been carefully studied and used beyond a collection of keywords. METHODS: In this study, we report the development of a new Interaction Network Ontology (INO) that classifies >800 interaction keywords and incorporates interaction terms from the PSI Molecular Interactions (PSI-MI) and Gene Ontology (GO). Using INO-based literature mining results, a modified Fisher’s exact test was established to analyze significantly over- and under-represented enriched gene-gene interaction types within a specific area. Such a strategy was applied to study the vaccine-mediated gene-gene interactions using all PubMed abstracts. The Vaccine Ontology (VO) and INO were used to support the retrieval of vaccine terms and interaction keywords from the literature. RESULTS: INO is aligned with the Basic Formal Ontology (BFO) and imports terms from 10 other existing ontologies. Current INO includes 540 terms. In terms of interaction-related terms, INO imports and aligns PSI-MI and GO interaction terms and includes over 100 newly generated ontology terms with ‘INO_’ prefix. A new annotation property, ‘has literature mining keywords’, was generated to allow the listing of different keywords mapping to the interaction types in INO. Using all PubMed documents published as of 12/31/2013, approximately 266,000 vaccine-associated documents were identified, and a total of 6,116 gene-pairs were associated with at least one INO term. Out of 78 INO interaction terms associated with at least five gene-pairs of the vaccine-associated sub-network, 14 terms were significantly over-represented (i.e., more frequently used) and 17 under-represented based on our modified Fisher’s exact test. These over-represented and under-represented terms share some common top-level terms but are distinct at the bottom levels of the INO hierarchy. The analysis of these interaction types and their associated gene-gene pairs uncovered many scientific insights. CONCLUSIONS: INO provides a novel approach for defining hierarchical interaction types and related keywords for literature mining. The ontology-based literature mining, in combination with an INO-based statistical interaction enrichment test, provides a new platform for efficient mining and analysis of topic-specific gene interaction networks. BioMed Central 2015-01-06 /pmc/articles/PMC4362819/ /pubmed/25785184 http://dx.doi.org/10.1186/2041-1480-6-2 Text en © Hur et al.; licensee BioMed Central. 2015 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Hur, Junguk
Özgür, Arzucan
Xiang, Zuoshuang
He, Yongqun
Development and application of an interaction network ontology for literature mining of vaccine-associated gene-gene interactions
title Development and application of an interaction network ontology for literature mining of vaccine-associated gene-gene interactions
title_full Development and application of an interaction network ontology for literature mining of vaccine-associated gene-gene interactions
title_fullStr Development and application of an interaction network ontology for literature mining of vaccine-associated gene-gene interactions
title_full_unstemmed Development and application of an interaction network ontology for literature mining of vaccine-associated gene-gene interactions
title_short Development and application of an interaction network ontology for literature mining of vaccine-associated gene-gene interactions
title_sort development and application of an interaction network ontology for literature mining of vaccine-associated gene-gene interactions
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4362819/
https://www.ncbi.nlm.nih.gov/pubmed/25785184
http://dx.doi.org/10.1186/2041-1480-6-2
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