Supervised learning classification models for prediction of plant virus encoded RNA silencing suppressors.

ABSTRACT: Viral encoded RNA silencing suppressor proteins interfere with the host RNA silencing machinery, facilitating viral infection by evading host immunity. In plant hosts, the viral proteins have several basic science implications and biotechnology applications. However in silico identification of these proteins is limited by their high sequence diversity. In this study we developed supervised learning based classification models for plant viral RNA silencing suppressor proteins in plant viruses. We developed four classifiers based on supervised learning algorithms: J48, Random Forest, LibSVM and Naïve Bayes algorithms, with enriched model learning by correlation based feature selection. Structural and physicochemical features calculated for experimentally verified primary protein sequences we

SUBMITTER: Jagga Z 

PROVIDER: S-EPMC4020838 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature