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

Table 1

The prediction performance of the RF model based on various features, evaluated by 10 cycles of 5-fold cross-validation on the MDset dataset.

Feature	Accuracy ± SD	Sensitivity ± SD	Specificity ± SD	MCC ± SD
PSSM-400	0.7967 ± 0.0062	0.7003 ± 0.0093	0.8894 ± 0.0075	0.620 ± 0.016
EIPP	0.8311 ± 0.0105	0.7487 ± 0.0071	0.9107 ± 0.0129	0.662 ± 0.021
CT	0.7482 ± 0.0092	0.6591 ± 0.0067	0.8406 ± 0.0153	0.5096 ± 0.015
EIPP + BP + NBP	0.8428 ± 0.0038	0.7573 ± 0.0082	0.9367 ± 0.0043	0.704 ± 0.008
CT + BP + NBP	0.7661 ± 0.0197	0.7034 ± 0.0132	0.8587 ± 0.0114	0.568 ± 0.026
EIPP + CT	0.8317 ± 0.0139	0.7482 ± 0.0068	0.9202 ± 0.0127	0.671 ± 0.018
EIPP + BP + NBP + CT	0.8573 ± 0.0117	0.7764 ± 0.0143	0.9424 ± 0.0062	0.729 ± 0.020