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Literature mining of protein-residue associations with graph rules learned through distant supervision
BACKGROUND: We propose a method for automatic extraction of protein-specific residue mentions from the biomedical literature. The method searches text for mentions of amino acids at specific sequence positions and attempts to correctly associate each mention with a protein also named in the text. Th...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3465209/ https://www.ncbi.nlm.nih.gov/pubmed/23046792 http://dx.doi.org/10.1186/2041-1480-3-S3-S2 |
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author | Ravikumar, KE Liu, Haibin Cohn, Judith D Wall, Michael E Verspoor, Karin |
author_facet | Ravikumar, KE Liu, Haibin Cohn, Judith D Wall, Michael E Verspoor, Karin |
author_sort | Ravikumar, KE |
collection | PubMed |
description | BACKGROUND: We propose a method for automatic extraction of protein-specific residue mentions from the biomedical literature. The method searches text for mentions of amino acids at specific sequence positions and attempts to correctly associate each mention with a protein also named in the text. The methods presented in this work will enable improved protein functional site extraction from articles, ultimately supporting protein function prediction. Our method made use of linguistic patterns for identifying the amino acid residue mentions in text. Further, we applied an automated graph-based method to learn syntactic patterns corresponding to protein-residue pairs mentioned in the text. We finally present an approach to automated construction of relevant training and test data using the distant supervision model. RESULTS: The performance of the method was assessed by extracting protein-residue relations from a new automatically generated test set of sentences containing high confidence examples found using distant supervision. It achieved a F-measure of 0.84 on automatically created silver corpus and 0.79 on a manually annotated gold data set for this task, outperforming previous methods. CONCLUSIONS: The primary contributions of this work are to (1) demonstrate the effectiveness of distant supervision for automatic creation of training data for protein-residue relation extraction, substantially reducing the effort and time involved in manual annotation of a data set and (2) show that the graph-based relation extraction approach we used generalizes well to the problem of protein-residue association extraction. This work paves the way towards effective extraction of protein functional residues from the literature. |
format | Online Article Text |
id | pubmed-3465209 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-34652092012-10-18 Literature mining of protein-residue associations with graph rules learned through distant supervision Ravikumar, KE Liu, Haibin Cohn, Judith D Wall, Michael E Verspoor, Karin J Biomed Semantics Research BACKGROUND: We propose a method for automatic extraction of protein-specific residue mentions from the biomedical literature. The method searches text for mentions of amino acids at specific sequence positions and attempts to correctly associate each mention with a protein also named in the text. The methods presented in this work will enable improved protein functional site extraction from articles, ultimately supporting protein function prediction. Our method made use of linguistic patterns for identifying the amino acid residue mentions in text. Further, we applied an automated graph-based method to learn syntactic patterns corresponding to protein-residue pairs mentioned in the text. We finally present an approach to automated construction of relevant training and test data using the distant supervision model. RESULTS: The performance of the method was assessed by extracting protein-residue relations from a new automatically generated test set of sentences containing high confidence examples found using distant supervision. It achieved a F-measure of 0.84 on automatically created silver corpus and 0.79 on a manually annotated gold data set for this task, outperforming previous methods. CONCLUSIONS: The primary contributions of this work are to (1) demonstrate the effectiveness of distant supervision for automatic creation of training data for protein-residue relation extraction, substantially reducing the effort and time involved in manual annotation of a data set and (2) show that the graph-based relation extraction approach we used generalizes well to the problem of protein-residue association extraction. This work paves the way towards effective extraction of protein functional residues from the literature. BioMed Central 2012-10-05 /pmc/articles/PMC3465209/ /pubmed/23046792 http://dx.doi.org/10.1186/2041-1480-3-S3-S2 Text en Copyright ©2012 Ravikumar etal.; 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 | Research Ravikumar, KE Liu, Haibin Cohn, Judith D Wall, Michael E Verspoor, Karin Literature mining of protein-residue associations with graph rules learned through distant supervision |
title | Literature mining of protein-residue associations with graph rules learned through distant supervision |
title_full | Literature mining of protein-residue associations with graph rules learned through distant supervision |
title_fullStr | Literature mining of protein-residue associations with graph rules learned through distant supervision |
title_full_unstemmed | Literature mining of protein-residue associations with graph rules learned through distant supervision |
title_short | Literature mining of protein-residue associations with graph rules learned through distant supervision |
title_sort | literature mining of protein-residue associations with graph rules learned through distant supervision |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3465209/ https://www.ncbi.nlm.nih.gov/pubmed/23046792 http://dx.doi.org/10.1186/2041-1480-3-S3-S2 |
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