TABLE 1.

Performance of protein-encoding methods with deep learning^a

Encoding method	Accuracy	Precision	Sensitivity	Specificity	F1 score	MCC
	Test data (706 sequences)
PC6	0.8895	0.9205	0.8527	0.9263	0.8853	0.7812
PC7	0.8782	0.9083	0.8414	0.9150	0.8735	0.7584
AC6	0.7040	0.7130	0.6827	0.7252	0.6975	0.4083
AC7	0.7507	0.7580	0.7365	0.7649	0.7471	0.5016
Embedding layer	0.8952	0.9091	0.8782	0.9122	0.8934	0.7908
Word2Vec	0.9065	0.9415	0.8669	0.9462	0.9027	0.8156
	External testing data (1,130 sequences)
PC6	0.8850	0.9035	0.8620	0.9080	0.8822	0.7707
PC7	0.8610	0.8849	0.8301	0.8920	0.8566	0.7235
AC6	0.7818	0.8534	0.7009	0.8695	0.7697	0.5760
AC7	0.7311	0.8152	0.6248	0.8464	0.7074	0.4808
Embedding layer	0.8690	0.9096	0.8195	0.9186	0.8622	0.7417
Word2Vec	0.8460	0.8574	0.8301	0.8619	0.8435	0.6924

^aTop three ranked methods for each index are presented using text format: first in boldface, second with underline, third in normal text format, and all the rest in italic.