Table 1.

Results of LOO tests using SVM classifiers based on different feature vectors

Class	Featurea	C|γb	IVA versus non-effectorc				IVB versus non-effector
			Sn	Sp	Acc	MCC	Sn	Sp	Acc	MCC
Single	AAC(20D)	10, 0.3	0.633	0.967	0.900	0.667	0.694	0.940	0.887	0.655
	DPC(400D)	10, 0.0125	0.533	0.992	0.900	0.663	0.674	0.932	0.877	0.625
	PSSM(400D)	10, 0.02	0.733	0.983	0.933	0.782	0.894	0.975	0.958	0.874
	PSSM_AC(200D, G = 10)	10, 0.1	0.667	0.967	0.907	0.691	0.829	0.957	0.929	0.790
Combined	AAC+DPC(420D)	10, 0.02	0.567	1.000	0.913	0.715	0.688	0.932	0.882	0.637
	PSSM+AAC(420D)	10, 0.02	0.767	0.975	0.933	0.784	0.897	0.976	0.959	0.878
	PSSM_AC+AAC(220D)	10, 0.05	0.667	0.975	0.913	0.712	0.848	0.966	0.941	0.824
	PSSM+PSSM_AC(600D)	10, 0.00125	0.733	0.958	0.913	0.720	0.897	0.964	0.949	0.852
	PSSM+PSSM_AC+AAC(620D)	10, 0.00125	0.700	0.983	0.927	0.759	0.897	0.964	0.950	0.853

^aAAC: amino acid composition; DPC: dipeptide composition; PSSM: PSSM composition; PSSM_AC: auto covariance transformation of PSSM profiles. The figure in the bracket refers to the dimensions of the features.

^bC and γ are the cost and the gamma parameter of the SVM, respectively. They were optimized based on a 10-fold cross-validation grid search.

^c120 non-effectors were selected randomly in the non-effector dataset as negative training samples for keeping the ratio of effectors to non-effectors at ∼1:4.