Open AccessPrediction of microRNA target genes using an efficient genetic algorithm‐based decision tree
Author(s) -
Rabiee-Ghahfarrokhi Behzad,
Rafiei Fariba,
Niknafs Ali Akbar,
Zamani Behzad
Publication year - 2015
Publication title -
febs open bio
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.718
H-Index - 31
ISSN - 2211-5463
DOI - 10.1016/j.fob.2015.10.003
Subject(s) - decision tree , microrna , computer science , machine learning , identification (biology) , tree (set theory) , artificial intelligence , key (lock) , coding (social sciences) , computational biology , function (biology) , selection (genetic algorithm) , gene , decision tree learning , algorithm , biology , genetics , mathematics , mathematical analysis , statistics , botany , computer security
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 incomplete 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.