Prediction of microRNA target genes using an efficient genetic algorithm-based decision tree

Behzad Rabiee-Ghahfarrokhi, Fariba Rafiei, Ali Akbar Niknafs, Behzad Zamani
2015 FEBS Open Bio  
MicroRNAs (miRNAs) are small, non-coding RNA molecules that regulate gene expression in almost all plants and animals. They play an important role in key processes, such as proliferation, apoptosis, and pathogen-host interactions. Nevertheless, the mechanisms by which miRNAs act are not fully understood. The first step toward unraveling the function of a particular miRNA is the identification of its direct targets. This step has shown to be quite challenging in animals primarily because of
more » ... plete complementarities between miRNA and target mRNAs. In recent years, the use of machine-learning techniques has greatly increased the prediction of miRNA targets, avoiding the need for costly and time-consuming experiments to achieve miRNA targets experimentally. Among the most important machine-learning algorithms are decision trees, which classify data based on extracted rules. In the present work, we used a genetic algorithm in combination with C4.5 decision tree for prediction of miRNA targets. We applied our proposed method to a validated human datasets. We nearly achieved 93.9% accuracy of classification, which could be related to the selection of best rules.
doi:10.1016/j.fob.2015.10.003 pmid:26649272 pmcid:PMC4643183 fatcat:byaapii3ejhvvejaw4vurmukyu