BELTracker: evidence sentence retrieval for BEL statements
Biological expression language (BEL) is one of the main formal representation models of biological networks. The primary source of information for curating biological networks in BEL representation has been literature. It remains a challenge to identify relevant articles and the corresponding eviden...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4865361/ https://www.ncbi.nlm.nih.gov/pubmed/27173525 http://dx.doi.org/10.1093/database/baw079 |
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author | Rastegar-Mojarad, Majid Komandur Elayavilli, Ravikumar Liu, Hongfang |
author_facet | Rastegar-Mojarad, Majid Komandur Elayavilli, Ravikumar Liu, Hongfang |
author_sort | Rastegar-Mojarad, Majid |
collection | PubMed |
description | Biological expression language (BEL) is one of the main formal representation models of biological networks. The primary source of information for curating biological networks in BEL representation has been literature. It remains a challenge to identify relevant articles and the corresponding evidence statements for curating and validating BEL statements. In this paper, we describe BELTracker, a tool used to retrieve and rank evidence sentences from PubMed abstracts and full-text articles for a given BEL statement (per the 2015 task requirements of BioCreative V BEL Task). The system is comprised of three main components, (i) translation of a given BEL statement to an information retrieval (IR) query, (ii) retrieval of relevant PubMed citations and (iii) finding and ranking the evidence sentences in those citations. BELTracker uses a combination of multiple approaches based on traditional IR, machine learning, and heuristics to accomplish the task. The system identified and ranked at least one fully relevant evidence sentence in the top 10 retrieved sentences for 72 out of 97 BEL statements in the test set. BELTracker achieved a precision of 0.392, 0.532 and 0.615 when evaluated with three criteria, namely full, relaxed and context criteria, respectively, by the task organizers. Our team at Mayo Clinic was the only participant in this task. BELTracker is available as a RESTful API and is available for public use. Database URL: http://www.openbionlp.org:8080/BelTracker/finder/Given_BEL_Statement |
format | Online Article Text |
id | pubmed-4865361 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-48653612016-05-13 BELTracker: evidence sentence retrieval for BEL statements Rastegar-Mojarad, Majid Komandur Elayavilli, Ravikumar Liu, Hongfang Database (Oxford) Original Article Biological expression language (BEL) is one of the main formal representation models of biological networks. The primary source of information for curating biological networks in BEL representation has been literature. It remains a challenge to identify relevant articles and the corresponding evidence statements for curating and validating BEL statements. In this paper, we describe BELTracker, a tool used to retrieve and rank evidence sentences from PubMed abstracts and full-text articles for a given BEL statement (per the 2015 task requirements of BioCreative V BEL Task). The system is comprised of three main components, (i) translation of a given BEL statement to an information retrieval (IR) query, (ii) retrieval of relevant PubMed citations and (iii) finding and ranking the evidence sentences in those citations. BELTracker uses a combination of multiple approaches based on traditional IR, machine learning, and heuristics to accomplish the task. The system identified and ranked at least one fully relevant evidence sentence in the top 10 retrieved sentences for 72 out of 97 BEL statements in the test set. BELTracker achieved a precision of 0.392, 0.532 and 0.615 when evaluated with three criteria, namely full, relaxed and context criteria, respectively, by the task organizers. Our team at Mayo Clinic was the only participant in this task. BELTracker is available as a RESTful API and is available for public use. Database URL: http://www.openbionlp.org:8080/BelTracker/finder/Given_BEL_Statement Oxford University Press 2016-05-12 /pmc/articles/PMC4865361/ /pubmed/27173525 http://dx.doi.org/10.1093/database/baw079 Text en © The Author(s) 2016. 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 Rastegar-Mojarad, Majid Komandur Elayavilli, Ravikumar Liu, Hongfang BELTracker: evidence sentence retrieval for BEL statements |
title | BELTracker: evidence sentence retrieval for BEL statements |
title_full | BELTracker: evidence sentence retrieval for BEL statements |
title_fullStr | BELTracker: evidence sentence retrieval for BEL statements |
title_full_unstemmed | BELTracker: evidence sentence retrieval for BEL statements |
title_short | BELTracker: evidence sentence retrieval for BEL statements |
title_sort | beltracker: evidence sentence retrieval for bel statements |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4865361/ https://www.ncbi.nlm.nih.gov/pubmed/27173525 http://dx.doi.org/10.1093/database/baw079 |
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