Table 3.

The prediction performance of phi and psi angles of using different local window sizes with four deep learning methods

Torsion Angle	Window sizea	Featuresb	DRNN	DReRBM	DNN	DRBM
phi	1	56	21.09	21.81	22.13	21.95
	3	168	20.52	20.77	21.49	21.07
	5	280	20.39	20.92	21.24	20.89
	7	392	20.39	21.03	21.22	20.84
	9	504	20.40	20.95	21.28	21.62
	11	616	20.49	20.88	21.04	21.57
	13	728	20.56	20.98	21.27	21.79
	15	840	20.63	21.12	21.19	21.69
	17	952	20.69	21.04	21.38	21.66
psi	1	56	31.68	32.93	33.55	33.02
	3	168	29.29	29.86	30.14	29.74
	5	280	28.96	29.94	29.25	28.92
	7	392	28.85	30.11	29.25	28.85
	9	504	28.86	29.94	29.38	29.61
	11	616	29.06	29.95	29.06	29.75
	13	728	29.27	30.13	29.38	30.19
	15	840	29.44	30.48	29.24	30.25
	17	952	29.72	30.36	29.54	30.33

^aNumber of window size range from 1 to 17

^bNumber of features as input for the deep learning model. For each residue, we used 7 kinds of features, represented by 56 numbers

The bold fond denotes the best result for each method