Table 5. Prediction results of peptide motifs binding to 14-3-3 isoforms by different regression techniques.

	Elastic Net		Simple Linear Regression		Support Vector Regression		Neural Network
	PCC	RMSE	PCC	RMSE	PCC	RMSE	PCC	RMSE
N-terminal
σ	0.84	252.31	0.82	261.69	0.79	283.16	0.60	368.39
β	0.72	229.12	0.69	238.40	0.70	236.18	0.57	270.43
ϵ	0.83	417.38	0.82	498.71	0.80	529.34	0.64	675.74
η	0.81	230.83	0.80	238.09	0.79	239.43	0.55	327.70
γ	0.86	470.08	0.86	474.16	0.83	506.56	0.59	745.79
τ	0.78	637.67	0.78	637.58	0.75	669.53	0.56	844.41
ζ	0.87	2087.20	0.88	2042.67	0.84	2306.04	0.56	3526.35
C-terminal
σ	0.77	269.13	0.76	273.19	0.74	279.54	0.64	321.78
β	0.63	245.10	0.61	247.96	0.59	252.64	0.51	269.64
ϵ	0.75	491.73	0.74	479.30	0.73	483.90	0.63	550.81
η	0.71	252.94	0.69	256.66	0.69	257.90	0.48	311.73
γ	0.79	463.40	0.79	459.40	0.80	454.01	0.68	558.68
τ	0.72	678.95	0.71	686.52	0.70	691.33	0.59	786.58
ζ	0.81	2365.42	0.80	2352.32	0.79	2429.84	0.66	3012.30