Table 2. Prediction accuracy of the SVR predictors based on eight different sequence encoding schemes that incorporate various combinations of global and local sequence features.

Sequence encoding scheme	Number of features	Number of support vectors	CC	RMSE	MAE	R square
PB	300	100134	0.64±0.02	1.90±0.10	1.41±0.08	0.456±0.02
SC	30	99021	0.65±0.03	1.86±0.11	1.34±0.10	0.474±0.03
PB+PP	345	100045	0.66±0.02	1.88±0.11	1.39±0.09	0.537±0.03
PB+SC	330	99662	0.69±0.03	1.77±0.10	1.31±0.08	0.539±0.03
PB+PP+SC	375	99682	0.70±0.03	1.76±0.11	1.30±0.09	0.540±0.03
PB+PP+SC+DISO	405	99719	0.70±0.03	1.75±0.11	1.29±0.09	0.539±0.03
PB+PP+SC+DISO+WL	407	99319	0.70±0.03	1.75±0.11	1.29±0.09	0.539±0.03
ALL	435	103631	0.71±0.03	1.74±0.10	1.28±0.08	0.541±0.03

All results were evaluated using 5-fold cross-validation method and expressed as mean±standard deviation.