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Wang, Y., Chen, X., Jiang, W., Li, L., Li, W., Yang, L., Liao, M., Lian, B., Lv, Y., Wang, S., Wang, S. and Li, X. (2011) Predicting human microRNA precursors based on an optimized feature subset generated by GA-SVM. Genomics, 98, 73-78. doi:10.1016/j.ygeno.2011.04.011

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• TITLE: A novel over-sampling method and its application to miRNA prediction

  AUTHORS: Xuan Tho Dang, Osamu Hirose, Thammakorn Saethang, Vu Anh Tran, Lan Anh T. Nguyen, Tu Kien T. Le, Mamoru Kubo, Yoichi Yamada, Kenji Satou

  KEYWORDS: Imbalanced Dataset; Over-Sampling; SMOTE; miRNA Classification

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.6 No.2A, February 27, 2013

  ABSTRACT: MicroRNAs (miRNAs) are short (~22nt) non-coding RNAs that play an indispensable role in gene regulation of many biological processes. Most of current computational, comparative, and non-comparative methods commonly classify human precursor micro- RNA (pre-miRNA) hairpins from both genome pseudo hairpins and other non-coding RNAs (ncRNAs). Although there were a few approaches achieving promising results in applying class imbalance learning methods, this issue has still not solved completely and successfully yet by the existing methods because of imbalanced class distribution in the datasets. For example, SMOTE is a famous and general over-sampling method addressing this problem, however in some cases it cannot improve or sometimes reduces classification performance. Therefore, we developed a novel over-sampling method named incre-mental- SMOTE to distinguish human pre-miRNA hairpins from both genome pseudo hairpins and other ncRNAs. Experimental results on pre-miRNA datasets from Batuwita et al. showed that our method achieved better Sensitivity and G-mean than the control (no over- sampling), SMOTE, and several successsors of modified SMOTE including safe-level-SMOTE and border-line-SMOTE. In addition, we also applied the novel method to five imbalanced benchmark datasets from UCI Machine Learning Repository and achieved improvements in Sensitivity and G-mean. These results suggest that our method outperforms SMOTE and several successors of it in various biomedical classification problems including miRNA classification.