Identification of non-coding RNAs with a new composite feature in the Hybrid Random Forest Ensemble algorithm.

ABSTRACT: To identify non-coding RNA (ncRNA) signals within genomic regions, a classification tool was developed based on a hybrid random forest (RF) with a logistic regression model to efficiently discriminate short ncRNA sequences as well as long complex ncRNA sequences. This RF-based classifier was trained on a well-balanced dataset with a discriminative set of features and achieved an accuracy, sensitivity and specificity of 92.11%, 90.7% and 93.5%, respectively. The selected feature set includes a new proposed feature, SCORE. This feature is generated based on a logistic regression function that combines five significant features-structure, sequence, modularity, structural robustness and coding potential-to enable improved characterization of long ncRNA (lncRNA) elements. The use of SCORE impro

SUBMITTER: Lertampaiporn S 

PROVIDER: S-EPMC4066759 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature