Research Article Sequence-Based Prediction of RNA-Binding Proteins Using Random Forest with Minimum Redundancy Maximum Relevance Feature Selection

permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The prediction of RNA-binding proteins is one of the most challenging problems in computation biology. Although some studies have investigated this problem, the accuracy of prediction is still not sufficient. In this study, a highly accuratemethodwas developed to predict RNA-binding proteins from amino acid sequences using random forests with the minimum redundancy maximum relevance (mRMR) method, followed by incremental feature selection (IFS). We incorporated features of conjoint triad features and three novel features: binding propensity (BP), nonbinding propensity (NBP), and evolutionary information combined with physicochemical properties (EIPP).The results showed that these novel features have important roles in improving the performance of the predictor. Using themRMR-IFSmethod, our predictor achieved the best performance (86.62 % accuracy and 0.737Matthews correlation coefficient). High prediction accuracy and successful prediction performance suggested that ourmethod can be a useful approach to identify RNA-binding proteins from sequence information. 1