Prediction of miRNA Based on miRNA Biogenesis via One-class SVM

作     者：LIU Yuan-ning YAN Wen ZHANG Hao LI Zhi LU Hui-jun LI Xin 

作者机构：College of Computer Science and Technology Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changehun 130012 P. R. China Institute of Military Veterinary Academy of Military Medical Sciences Changchun 130012 P. R. China Changchun University of Science ＆ Technology Changchun 130022 P. R. China 

出 版 物：《Chemical Research in Chinese Universities》 (高等学校化学研究（英文版）)

年 卷 期：2010年第26卷第5期

页      面：803-809页

核心收录：

学科分类：0710[理学-生物学] 071010[理学-生物化学与分子生物学] 081704[工学-应用化学] 07[理学] 08[工学] 0817[工学-化学工程与技术] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：Supported by the National Natural Science Foundation of China(No.60971089) 

主　　题：miRNAs Hairpin One-class classification miRNAs Biogenesis 

摘      要：MicroRNAs are a class of small, single-stranded RNAs which are produced by non-protein-coding RNA genes with a length of 21-29 nt. They regulate the expression of protein-encoding genes at the post-transcriptional level and the degradation ofmRNAs by base pairing to mRNAs. Mature miRNAs are processed from 60-90 nt RNA hairpin structures called pre-miRNAs. At present, most of the machine learning computational methods for pre-miRNAs prediction are based on two-class SVM and use structural information of pre-miRNA hairpins. Those methods share a common feature that all of them need a negative dataset in the training dataset and feature selection in both training and testing dataset. In order to avoid selecting false negative examples of miRNA hairpins in the training dataset which may mislead the classifiers, we presented a microRNA prediction algorithm called MirBio based on miRNAs Biogenesis which is trained only on the information of the positive miRNAs class to predict miRNAs. It can predict both pre-miRNAs and miRNAs and get a relatively satisfying result in this study.