MeInfoText 2.0: gene methylation and cancer relation extraction from biomedical literature
Resource
BMC BIOINFORMATICS, 12, 471
Journal
BMC Bioinformatics
Pages
471
Date Issued
2011
Date
2011
Author(s)
Fang, Yu-Ching
Lai, Po-Ting
Dai, Hong-Jie
Hsu, Wen-Lian
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/.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. ? 2011 Fang et al; licensee BioMed Central Ltd.
SDGs
Other Subjects
10-fold cross-validation; Biomedical literature; Cancer development; Learning-based approach; Maximum entropy models; Maximum probability; Relation extraction; Scientific studies; Alkylation; Data mining; Genes; Learning algorithms; Learning systems; Methylation; Pattern matching; Diseases; article; artificial intelligence; computer program; CpG island; data mining; DNA methylation; genetic epigenesis; genetics; human; methodology; neoplasm; Artificial Intelligence; CpG Islands; Data Mining; DNA Methylation; Epigenesis, Genetic; Humans; Neoplasms; Software
Type
journal article
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