Prediction of protein-protein interaction sites by random forest algorithm with mRMR and IFS.

ABSTRACT: Prediction of protein-protein interaction (PPI) sites is one of the most challenging problems in computational biology. Although great progress has been made by employing various machine learning approaches with numerous characteristic features, the problem is still far from being solved. In this study, we developed a novel predictor based on Random Forest (RF) algorithm with the Minimum Redundancy Maximal Relevance (mRMR) method followed by incremental feature selection (IFS). We incorporated features of physicochemical/biochemical properties, sequence conservation, residual disorder, secondary structure and solvent accessibility. We also included five 3D structural features to predict protein-protein interaction sites and achieved an overall accuracy of 0.672997 and MCC of 0.347977. Feat

SUBMITTER: Li BQ 

PROVIDER: S-EPMC3429425 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature