Jump to main content
Jump to site search

Issue 8, 2017
Previous Article Next Article

Identification of microRNA precursors using reduced and hybrid features

Author affiliations


MicroRNAs (also called miRNAs) are a group of short non-coding RNA molecules. They play a vital role in the gene expression of transcriptional and post-transcriptional processes. However, abnormality of their expression has been observed in cancer, heart diseases and nervous system disorders. Therefore for basic research and microRNA based therapy, it is imperative to separate real pre-miRNAs from false ones (hairpin sequences similar to pre-miRNA stem loops). Different conservation and machine learning methods have been applied for the identification of miRNAs. However, machine learning algorithms have gained more popularity than conservative based algorithms in terms of sensitivity and overall performance. Due to the avalanche of RNA sequences discovered in a post-genomic age, it is necessary to construct a predictor for the identification of pre-microRNAs in humans. We have developed a predictor called MicroR-Pred in which the RNA sequences are formulated by a hybrid feature vector. The novelty of the new predictor is in the use of the partial least squares technique followed by the Random Forest and SVM (Support Vector Machine) algorithms for dimension reduction and classification. The performance of the MicroR-Pred model is quite promising compared to other state-of-the-art miRNA predictors. It has achieved 88.40% and 93.90% accuracies for RF and SVM.

Graphical abstract: Identification of microRNA precursors using reduced and hybrid features

Back to tab navigation

Supplementary files

Article information

24 Feb 2017
15 Jun 2017
First published
15 Jun 2017

Mol. BioSyst., 2017,13, 1640-1645
Article type

Identification of microRNA precursors using reduced and hybrid features

A. Khan, S. Shah, F. Wahid, F. G. Khan and S. Jabeen, Mol. BioSyst., 2017, 13, 1640
DOI: 10.1039/C7MB00115K

Search articles by author