HMMBinder: DNA-Binding Protein Prediction Using HMM Profile Based Features

Table 1

Comparison of performances of different features and SVM kernels on the benchmark dataset using 10-fold cross validation.

Features	Accuracy	Sensitivity	Specificity	auPR	MCC	auROC
SVM with linear kernel
HMM-Monogram	76.77%	0.8420	0.6976	0.6931	0.5367	0.8358
PSSM-Monogram	74.74%	0.6636	0.8362	0.8368	0.5040	0.8105
HMM-Bigram	70.59%	0.7071	0.7049	0.7060	0.4095	0.7511
PSSM-Bigram	62.20%	0.6454	0.5973	0.6025	0.2502	0.6703
HMM (Mono + Bi)	82.87%	0.8150	0.8415	0.8428	0.6538	0.8639
PSSM (Mono + Bi)	72.40%	0.7364	0.7120	0.7136	0.4486	0.8028
SVM with RBF kernel
HMM-Monogram	78.83%	0.8227	0.7559	0.7535	0.5761	0.8667
PSSM-Monogram	73.71%	0.6890	0.7880	0.7903	0.4771	0.8121
HMM-Bigram	76.68%	0.7052	0.8251	0.8253	0.5283	0.8318
PSSM-Bigram	74.92%	0.7490	0.7495	0.7516	0.4966	0.8166
HMM (Mono + Bi)	77.43%	0.7129	0.8324	0.8329	0.5440	0.8496
PSSM (Mono + Bi)	72.40%	0.7363	0.7120	0.7136	0.4486	0.8028
Random Forest
HMM-Monogram	74.44%	0.7938	0.6976	0.6936	0.4871	0.8243
PSSM-Monogram	66.14%	0.7290	0.5895	0.5862	0.3173	0.7332
HMM-Bigram	72.19%	0.7553	0.6903	0.6880	0.4400	0.8273
PSSM-Bigram	71.00%	0.7854	0.6300	0.6305	0.4174	0.7833
HMM (Mono + Bi)	74.43%	0.7938	0.6976	0.6931	0.4871	0.8218
PSSM (Mono + Bi)	72.68%	0.7909	0.6589	0.6645	0.4557	0.7698
AdaBoost
HMM-Monogram	73.31%	0.7013	0.7632	0.7603	0.4579	0.8026
PSSM-Monogram	67.07%	0.7654	0.5703	0.5737	0.3448	0.7157
HMM-Bigram	73.97%	0.7360	0.7432	0.7396	0.4762	0.8063
PSSM-Bigram	70.53%	0.7436	0.6647	0.6708	0.4116	0.7710
HMM (Mono + Bi)	78.00%	0.7803	0.7795	0.7732	0.5532	0.8577
PSSM (Mono + Bi)	70.07%	0.7327	0.6666	0.6687	0.4005	0.7887