Title: MicroRNAfold: pre-microRNA secondary structure prediction based on modified NCM model with thermodynamics-based scoring strategy
Author: Dianwei Han; Jun Zhang; Guiliang Tang
Address: Department of Computer Science, University of Kentucky, Lexington, KY 40506-0046, USA ' Department of Computer Science, University of Kentucky, Lexington, KY 40506-0046, USA ' Department of Plant and Soil Sciences, University of Kentucky, Lexington, KY 40546-0236, USA
Journal: Int. J. of Data Mining and Bioinformatics, 2012 Vol.6, No.3, pp.272 - 291
Abstract: An accurate prediction of the pre-microRNA secondary structure is important in miRNA informatics. Based on a recently proposed model, nucleotide cyclic motifs (NCM), to predict RNA secondary structure, we propose and implement a Modified NCM (MNCM) model with a physics-based scoring strategy to tackle the problem of pre-microRNA folding. Our microRNAfold is implemented using a global optimal algorithm based on the bottom-up local optimal solutions. Our experimental results show that microRNAfold outperforms the current leading prediction tools in terms of True Negative rate, False Negative rate, Specificity, and Matthews coefficient ratio.
Keywords: thermodynamics; scoring function; RNA folding; pre-microRNA secondary structure prediction; bottom-up local optimal solutions; NCM model; nucleotide cyclic motifs; scoring strategy; bioinformatics.