title: Ensemble learning can significantly improve human microRNA target prediction.
abstract: MicroRNAs (miRNAs) regulate the function of their target genes by down-regulating gene expression, participating in various biological processes. Since the discovery of the first miRNA, computational tools have been essential to predict targets of given miRNAs that can be biologically verified. The precise mechanism underlying miRNA-mRNA interaction has not yet been elucidated completely, and it is still difficult to predict miRNA targets computationally in a robust fashion, despite the large number of in silico prediction methodologies in existence. Because of this limitation, different target prediction tools often report different and (occasionally conflicting) sets of targets. Therefore, we propose a novel target prediction methodology called stacking-based miRNA interaction learner ensemble (SMILE) that employs the concept of stacked generalization (stacking), which is a type of ensemble learning that integrates the outcomes of individual prediction tools with the aim of surpassing the performance of the individual tools. We tested the proposed SMILE method on human miRNA-mRNA interaction data derived from public databases. In our experiments, SMILE improved the accuracy of the target prediction significantly in terms of the area under the receiver operating characteristic curve. Any new target prediction tool can easily be incorporated into the proposed methodology as a component learner, and we anticipate that SMILE will provide a flexible and effective framework for elucidating in vivo miRNA-mRNA interaction.
authors.affiliation: Department of Electrical and Computer Engineering, Seoul National University, Seoul 151-744, Republic of Korea; Department of IT Convergence, Korea University, Seoul 156-713, Republic of Korea.
authors.forename: Seunghak
authors.initials: S
authors.lastname: Yu
authors.affiliation: Department of Electrical and Computer Engineering, Seoul National University, Seoul 151-744, Republic of Korea.
authors.forename: Juho
authors.initials: J
authors.lastname: Kim
authors.affiliation: RNA Biopharmacy Laboratory, College of Pharmacy, Chung-Ang University, Seoul 156-756, Republic of Korea.
authors.forename: Hyeyoung
authors.initials: H
authors.lastname: Min
authors.affiliation: Department of Electrical and Computer Engineering, Seoul National University, Seoul 151-744, Republic of Korea; Bioinformatics Institute, Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 151-747, Republic of Korea. Electronic address: sryoon@snu.ac.kr.
authors.forename: Sungroh
authors.initials: S
authors.lastname: Yoon
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D035683:MicroRNAs / Q000235:genetics*; D012333:RNA, Messenger / Q000235:genetics*; D017423:Sequence Analysis, RNA / Q000379:methods*
chemical_list: D035683:MicroRNAs; D012333:RNA, Messenger
keywords: Algorithm; Machine learning; MicroRNA; Sequence analysis; Target prediction