title: lncRScan-SVM: A Tool for Predicting Long Non-Coding RNAs Using Support Vector Machine.
abstract: Functional long non-coding RNAs (lncRNAs) have been bringing novel insight into biological study, however it is still not trivial to accurately distinguish the lncRNA transcripts (LNCTs) from the protein coding ones (PCTs). As various information and data about lncRNAs are preserved by previous studies, it is appealing to develop novel methods to identify the lncRNAs more accurately. Our method lncRScan-SVM aims at classifying PCTs and LNCTs using support vector machine (SVM). The gold-standard datasets for lncRScan-SVM model training, lncRNA prediction and method comparison were constructed according to the GENCODE gene annotations of human and mouse respectively. By integrating features derived from gene structure, transcript sequence, potential codon sequence and conservation, lncRScan-SVM outperforms other approaches, which is evaluated by several criteria such as sensitivity, specificity, accuracy, Matthews correlation coefficient (MCC) and area under curve (AUC). In addition, several known human lncRNA datasets were assessed using lncRScan-SVM. LncRScan-SVM is an efficient tool for predicting the lncRNAs, and it is quite useful for current lncRNA study.
authors.affiliation: School of Information Engineering, Yangzhou University, Yangzhou, Jiangsu Province, China; Jiangsu Co-innovation Center for Prevention and Control of Important Animal Infectious Diseases and Zoonoses, Yangzhou University, Yangzhou, Jiangsu Province, China.
authors.forename: Lei
authors.initials: L
authors.lastname: Sun
authors.affiliation: School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, JiangSu Province, China.
authors.forename: Hui
authors.initials: H
authors.lastname: Liu
authors.affiliation: School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, JiangSu Province, China.
authors.forename: Lin
authors.initials: L
authors.lastname: Zhang
authors.affiliation: Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu Province, China.
authors.forename: Jia
authors.initials: J
authors.lastname: Meng
mesh_terms: D060388:Support Vector Machine