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

Yu-Ching Fang1, Po-Ting Lai2, Hong-Jie Dai3 and Wen-Lian Hsu3*

Author Affiliations

1 Institute of Molecular and Cellular Biology, National Taiwan University, Taipei, Taiwan

2 Department of Computer Science, National Chengchi University, Taipei, Taiwan

3 Institute of Information Science, Academia Sinica, Nankang, Taipei, Taiwan

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BMC Bioinformatics 2011, 12:471  doi:10.1186/1471-2105-12-471

Published: 14 December 2011

Abstract

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/ webcite.

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.