MeInfoText 2.0: gene methylation and cancer relation extraction from biomedical literature

BMC Bioinformatics

Database

Po-Ting Lai¹  Wen-Lian Hsu²  Hong-Jie Dai²  Yu-Ching Fang³ 

[1] Department of Computer Science, National Chengchi University, Taipei, Taiwan;Institute of Information Science, Academia Sinica, Nankang, Taipei, Taiwan;Institute of Molecular and Cellular Biology, National Taiwan University, Taipei, Taiwan;

关键词: Breast Cancer;    Association Rule;    Gene Methylation;    Relation Extraction;    Name Entity;   

DOI  :  10.1186/1471-2105-12-471

 received in 2011-02-23, accepted in 2011-12-14,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundDNA 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.DescriptionTwo 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/.ConclusionThe 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.

【 授权许可】

Unknown   
© Fang et al; licensee BioMed Central Ltd. 2011. 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/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.