Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2016 (2016), Article ID 9565689, 11 pages
http://dx.doi.org/10.1155/2016/9565689
Research Article

BP Neural Network Could Help Improve Pre-miRNA Identification in Various Species

1School of Computer Science and Technology, Tianjin University, Tianjin 300350, China
2School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, China
3School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
4State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin 300074, China

Received 17 May 2016; Revised 5 July 2016; Accepted 17 July 2016

Academic Editor: Xing Chen

Copyright © 2016 Limin Jiang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

MicroRNAs (miRNAs) are a set of short (21–24 nt) noncoding RNAs that play significant regulatory roles in cells. In the past few years, research on miRNA-related problems has become a hot field of bioinformatics because of miRNAs’ essential biological function. miRNA-related bioinformatics analysis is beneficial in several aspects, including the functions of miRNAs and other genes, the regulatory network between miRNAs and their target mRNAs, and even biological evolution. Distinguishing miRNA precursors from other hairpin-like sequences is important and is an essential procedure in detecting novel microRNAs. In this study, we employed backpropagation (BP) neural network together with 98-dimensional novel features for microRNA precursor identification. Results show that the precision and recall of our method are 95.53% and 96.67%, respectively. Results further demonstrate that the total prediction accuracy of our method is nearly 13.17% greater than the state-of-the-art microRNA precursor prediction software tools.