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A semi-automated methodology for finding lipid-related GO terms
Motivation: Although semantic similarity in Gene Ontology (GO) and other approaches may be used to find similar GO terms, there is yet a method to systematically find a class of GO terms sharing a common property with high accuracy (e.g. involving human curation). Results: We have developed a method...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4160098/ https://www.ncbi.nlm.nih.gov/pubmed/25209026 http://dx.doi.org/10.1093/database/bau089 |
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author | Fan, Mengyuan Low, Hong Sang Wenk, Markus R. Wong, Limsoon |
author_facet | Fan, Mengyuan Low, Hong Sang Wenk, Markus R. Wong, Limsoon |
author_sort | Fan, Mengyuan |
collection | PubMed |
description | Motivation: Although semantic similarity in Gene Ontology (GO) and other approaches may be used to find similar GO terms, there is yet a method to systematically find a class of GO terms sharing a common property with high accuracy (e.g. involving human curation). Results: We have developed a methodology to address this issue and applied it to identify lipid-related GO terms, owing to the important and varied roles of lipids in many biological processes. Our methodology finds lipid-related GO terms in a semi-automated manner, requiring only moderate manual curation. We first obtain a list of lipid-related gold-standard GO terms by keyword search and manual curation. Then, based on the hypothesis that co-annotated GO terms share similar properties, we develop a machine learning method that expands the list of lipid-related terms from the gold standard. Those terms predicted most likely to be lipid related are examined by a human curator following specific curation rules to confirm the class labels. The structure of GO is also exploited to help reduce the curation effort. The prediction and curation cycle is repeated until no further lipid-related term is found. Our approach has covered a high proportion, if not all, of lipid-related terms with relatively high efficiency. Database URL: http://compbio.ddns.comp.nus.edu.sg/∼lipidgo |
format | Online Article Text |
id | pubmed-4160098 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-41600982014-09-11 A semi-automated methodology for finding lipid-related GO terms Fan, Mengyuan Low, Hong Sang Wenk, Markus R. Wong, Limsoon Database (Oxford) Original Article Motivation: Although semantic similarity in Gene Ontology (GO) and other approaches may be used to find similar GO terms, there is yet a method to systematically find a class of GO terms sharing a common property with high accuracy (e.g. involving human curation). Results: We have developed a methodology to address this issue and applied it to identify lipid-related GO terms, owing to the important and varied roles of lipids in many biological processes. Our methodology finds lipid-related GO terms in a semi-automated manner, requiring only moderate manual curation. We first obtain a list of lipid-related gold-standard GO terms by keyword search and manual curation. Then, based on the hypothesis that co-annotated GO terms share similar properties, we develop a machine learning method that expands the list of lipid-related terms from the gold standard. Those terms predicted most likely to be lipid related are examined by a human curator following specific curation rules to confirm the class labels. The structure of GO is also exploited to help reduce the curation effort. The prediction and curation cycle is repeated until no further lipid-related term is found. Our approach has covered a high proportion, if not all, of lipid-related terms with relatively high efficiency. Database URL: http://compbio.ddns.comp.nus.edu.sg/∼lipidgo Oxford University Press 2014-09-10 /pmc/articles/PMC4160098/ /pubmed/25209026 http://dx.doi.org/10.1093/database/bau089 Text en © The Author(s) 2014. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ 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 reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Fan, Mengyuan Low, Hong Sang Wenk, Markus R. Wong, Limsoon A semi-automated methodology for finding lipid-related GO terms |
title | A semi-automated methodology for finding lipid-related GO terms |
title_full | A semi-automated methodology for finding lipid-related GO terms |
title_fullStr | A semi-automated methodology for finding lipid-related GO terms |
title_full_unstemmed | A semi-automated methodology for finding lipid-related GO terms |
title_short | A semi-automated methodology for finding lipid-related GO terms |
title_sort | semi-automated methodology for finding lipid-related go terms |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4160098/ https://www.ncbi.nlm.nih.gov/pubmed/25209026 http://dx.doi.org/10.1093/database/bau089 |
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