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Table 1.

Optimization of Neural Network Architecture (Input Parameters vs. Performance on the EGF-Like Domain Type[i])

			Training set		Test set		Total

	Parameter	No		tp	fp	fn		tn	C	tp	fp	fn	tn	C	tp	fp	fn	tn	C

1	NSD, AVS	2		242	23	49		311	0.77	125	3	20	137	0.85	360	14	76	460	0.81

2	NSD, AVS, P $_{nfp}^{NSD}$ (NSD), P $_{nfp}^{AVS}$ (AVS)	4		284	4	7		330	0.97	142	3	3	137	0.96	426	8	10	466	0.96

3	NSD, AVS, P $_{p}^{NSD}$ (NSD),P $_{p}^{AVS}$ (AVS)	2		291	2	0		332	0.99	145	3	0	137	0.98	434	4	2	470	0.99

4	NSD, AVS, P $_{p}^{NSD}$ (NSD), P $_{p}^{AVS}$ (AVS), P $_{nfp}^{NSD}$ (NSD), P $_{nfp}^{AVS}$ (AVS)	4		284	4	7		330	0.97	142	3	3	137	0.96	426	8	10	466	0.96

[i] tp, True positives; fp, false positives; tn, true negatives; fn, false negatives.

[ii] C is the Matthews (Pearson) correlation coefficient (Matthews 1975),

C = \frac{tp \times tn - fp \times fn}{\sqrt{(tp + fn) (tp + fp) (tn + fp) (tn + fn)}} if fp = fn = 0, C = 1.000 .

Each neural network contained one hidden layer with the same number of elements as the number of input parameters. Note that the Total values were obtained by retraining the ANNs on the entire dataset, so these values are not necessarily equal to the sum of the corresponding Training Set and Test Set values.