Abstract
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.
Highlights
DNA methylation is regarded as a potential biomarker in the diagnosis and treatment of cancer
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 pages retrieved by MeInfoText 2.0 for the above gene methylation profile are similar to the results reported by Esteller et al [21]
Summary
MeInfoText 2.0 provides more accurate information about gene methylation-cancer associations discussed in a large number of studies. To the best of our knowledge, this is the first study that uses machine learning, a domain dictionary and pattern matching to extract genetic-epigenetic relations. Such relations are important for determining if unique profiles exist for specific types of cancer, and assessing how to improve cancer detection and treatment by using DNA methylation biomarkers. The study is the first attempt to create a gene methylation-cancer corpus. Operating system(s): platform independent License: the database website is freely accessible
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