MiRenSVM: towards better prediction of microRNA precursors using an ensemble SVM classifier with multi-loop features.

ABSTRACT:

Background

MicroRNAs (simply miRNAs) are derived from larger hairpin RNA precursors and play essential regular roles in both animals and plants. A number of computational methods for miRNA genes finding have been proposed in the past decade, yet the problem is far from being tackled, especially when considering the imbalance issue of known miRNAs and unidentified miRNAs, and the pre-miRNAs with multi-loops or higher minimum free energy (MFE). This paper presents a new computational approach, miRenSVM, for finding miRNA genes. Aiming at better prediction performance, an ensemble support vector machine (SVM) classifier is established to deal with the imbalance issue, and multi-loop features are included for identifying those pre-miRNAs with multi-loops.

Results

We collected a

SUBMITTER: Ding J 

PROVIDER: S-EPMC3024864 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature