title: Heterogeneous ensemble approach with discriminative features and modified-SMOTEbagging for pre-miRNA classification.
abstract: An ensemble classifier approach for microRNA precursor (pre-miRNA) classification was proposed based upon combining a set of heterogeneous algorithms including support vector machine (SVM), k-nearest neighbors (kNN) and random forest (RF), then aggregating their prediction through a voting system. Additionally, the proposed algorithm, the classification performance was also improved using discriminative features, self-containment and its derivatives, which have shown unique structural robustness characteristics of pre-miRNAs. These are applicable across different species. By applying preprocessing methods--both a correlation-based feature selection (CFS) with genetic algorithm (GA) search method and a modified-Synthetic Minority Oversampling Technique (SMOTE) bagging rebalancing method--improvement in the performance of this ensemble was observed. The overall prediction accuracies obtained via 10 runs of 5-fold cross validation (CV) was 96.54%, with sensitivity of 94.8% and specificity of 98.3%-this is better in trade-off sensitivity and specificity values than those of other state-of-the-art methods. The ensemble model was applied to animal, plant and virus pre-miRNA and achieved high accuracy, >93%. Exploiting the discriminative set of selected features also suggests that pre-miRNAs possess high intrinsic structural robustness as compared with other stem loops. Our heterogeneous ensemble method gave a relatively more reliable prediction than those using single classifiers. Our program is available at http://ncrna-pred.com/premiRNA.html.
authors.affiliation: Biological Engineering Program, King Mongkut's University of Technology Thonburi, Bang Mod, Thung Khru, Bangkok 10140, Thailand.
authors.forename: Supatcha
authors.initials: S
authors.lastname: Lertampaiporn
authors.forename: Chinae
authors.initials: C
authors.lastname: Thammarongtham
authors.forename: Chakarida
authors.initials: C
authors.lastname: Nukoolkit
authors.forename: Boonserm
authors.initials: B
authors.lastname: Kaewkamnerdpong
authors.forename: Marasri
authors.initials: M
authors.lastname: Ruengjitchatchawalya
mesh_terms: D000465:Algorithms; D020029:Base Pairing; D006801:Humans; D035683:MicroRNAs / Q000737:chemistry / Q000145:classification*; D012322:RNA Precursors / Q000737:chemistry / Q000145:classification*; D018749:RNA, Plant / Q000737:chemistry / Q000145:classification; D012367:RNA, Viral / Q000737:chemistry / Q000145:classification; D012680:Sensitivity and Specificity
chemical_list: D035683:MicroRNAs; D012322:RNA Precursors; D018749:RNA, Plant; D012367:RNA, Viral