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MeInfoText 2.0: gene methylation and cancer relation extraction from biomedical literature
BACKGROUND: DNA methylation is regarded as a potential biomarker in the diagnosis and treatment of cancer. The relations between aberrant gene methylation and cancer development have been identified by a number of recent scientific studies. In a previous work, we used co-occurrences to mine those as...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3266364/ https://www.ncbi.nlm.nih.gov/pubmed/22168213 http://dx.doi.org/10.1186/1471-2105-12-471 |
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author | Fang, Yu-Ching Lai, Po-Ting Dai, Hong-Jie Hsu, Wen-Lian |
author_facet | Fang, Yu-Ching Lai, Po-Ting Dai, Hong-Jie Hsu, Wen-Lian |
author_sort | Fang, Yu-Ching |
collection | PubMed |
description | BACKGROUND: DNA methylation is regarded as a potential biomarker in the diagnosis and treatment of cancer. The relations between aberrant gene methylation and cancer development have been identified by a number of recent scientific studies. In a previous work, we used co-occurrences to mine those associations and compiled the MeInfoText 1.0 database. To reduce the amount of manual curation and improve the accuracy of relation extraction, we have now developed MeInfoText 2.0, which uses a machine learning-based approach to extract gene methylation-cancer relations. DESCRIPTION: Two maximum entropy models are trained to predict if aberrant gene methylation is related to any type of cancer mentioned in the literature. After evaluation based on 10-fold cross-validation, the average precision/recall rates of the two models are 94.7/90.1 and 91.8/90% respectively. MeInfoText 2.0 provides the gene methylation profiles of different types of human cancer. The extracted relations with maximum probability, evidence sentences, and specific gene information are also retrievable. The database is available at http://bws.iis.sinica.edu.tw:8081/MeInfoText2/. CONCLUSION: The previous version, MeInfoText, was developed by using association rules, whereas MeInfoText 2.0 is based on a new framework that combines machine learning, dictionary lookup and pattern matching for epigenetics information extraction. The results of experiments show that MeInfoText 2.0 outperforms existing tools in many respects. To the best of our knowledge, this is the first study that uses a hybrid approach to extract gene methylation-cancer relations. It is also the first attempt to develop a gene methylation and cancer relation corpus. |
format | Online Article Text |
id | pubmed-3266364 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-32663642012-01-26 MeInfoText 2.0: gene methylation and cancer relation extraction from biomedical literature Fang, Yu-Ching Lai, Po-Ting Dai, Hong-Jie Hsu, Wen-Lian BMC Bioinformatics Database BACKGROUND: DNA methylation is regarded as a potential biomarker in the diagnosis and treatment of cancer. The relations between aberrant gene methylation and cancer development have been identified by a number of recent scientific studies. In a previous work, we used co-occurrences to mine those associations and compiled the MeInfoText 1.0 database. To reduce the amount of manual curation and improve the accuracy of relation extraction, we have now developed MeInfoText 2.0, which uses a machine learning-based approach to extract gene methylation-cancer relations. DESCRIPTION: Two maximum entropy models are trained to predict if aberrant gene methylation is related to any type of cancer mentioned in the literature. After evaluation based on 10-fold cross-validation, the average precision/recall rates of the two models are 94.7/90.1 and 91.8/90% respectively. MeInfoText 2.0 provides the gene methylation profiles of different types of human cancer. The extracted relations with maximum probability, evidence sentences, and specific gene information are also retrievable. The database is available at http://bws.iis.sinica.edu.tw:8081/MeInfoText2/. CONCLUSION: The previous version, MeInfoText, was developed by using association rules, whereas MeInfoText 2.0 is based on a new framework that combines machine learning, dictionary lookup and pattern matching for epigenetics information extraction. The results of experiments show that MeInfoText 2.0 outperforms existing tools in many respects. To the best of our knowledge, this is the first study that uses a hybrid approach to extract gene methylation-cancer relations. It is also the first attempt to develop a gene methylation and cancer relation corpus. BioMed Central 2011-12-14 /pmc/articles/PMC3266364/ /pubmed/22168213 http://dx.doi.org/10.1186/1471-2105-12-471 Text en Copyright ©2011 Fang et al; 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 | Database Fang, Yu-Ching Lai, Po-Ting Dai, Hong-Jie Hsu, Wen-Lian MeInfoText 2.0: gene methylation and cancer relation extraction from biomedical literature |
title | MeInfoText 2.0: gene methylation and cancer relation extraction from biomedical literature |
title_full | MeInfoText 2.0: gene methylation and cancer relation extraction from biomedical literature |
title_fullStr | MeInfoText 2.0: gene methylation and cancer relation extraction from biomedical literature |
title_full_unstemmed | MeInfoText 2.0: gene methylation and cancer relation extraction from biomedical literature |
title_short | MeInfoText 2.0: gene methylation and cancer relation extraction from biomedical literature |
title_sort | meinfotext 2.0: gene methylation and cancer relation extraction from biomedical literature |
topic | Database |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3266364/ https://www.ncbi.nlm.nih.gov/pubmed/22168213 http://dx.doi.org/10.1186/1471-2105-12-471 |
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