Table 2.

Result for drug target protein prediction using machine learning methods

SVM	Recall	Precision	F1
Set A, W	0.7326	0.6594	0.6941
Set A, W ′	0.7516	0.7422	0.7469
Set A, W+N	0.7947	0.6681	0.7259
Set A, W ′ + N ′	0.8137	0.6982	0.7515
Set B, W	0.7866	0.6416	0.7067
Set B, W ′	0.7374	0.6496	0.6907
Set B, W+N	0.7424	0.6585	0.6979
Set B, W ′ + N ′	0.8018	0.6580	0.7228
Set C, W	0.7516	0.7808	0.7659
Set C, W ′	0.7972	0.8003	0.7987
Set C, W+N	0.8137	0.7965	0.8050
Set C, W ′ + N ′	0.8409	0.8207	0.8307
Set D, W	0.7820	0.7367	0.7587
Set D, W ′	0.8083	0.7588	0.7828
Set D, W+N	0.8120	0.7500	0.7798
Set D, W ′ + N ′	0.8271	0.7710	0.7981
RF
Set A, W	0.7541	0.7682	0.7605
Set A, W ′	0.6483	0.8130	0.7260
Set A, W+N	0.7936	0.6763	0.7299
Set A, W ′ + N ′	0.8229	0.6986	0.7556
Set B, W	0.7821	0.6547	0.7124
Set B, W ′	0.7490	0.6493	0.6953
Set B, W+N	0.7551	0.7805	0.7677
Set B, W ′ + N ′	0.8076	0.6767	0.7363
Set C, W	0.7847	0.7358	0.7589
Set C, W ′	0.8165	0.7960	0.8057
Set C, W+N	0.8292	0.8118	0.8200
Set C, W ′ + N ′	0.8509	0.8218	0.8354
Set D, W	0.7885	0.7409	0.7636
Set D, W ′	0.8343	0.7564	0.7934
Set D, W+N	0.8305	0.7550	0.7908
Set D, W ′ + N ′	0.8382	0.7818	0.8088

Feature sets W and N represent widely used and newly proposed properties, respectively. W ^′ and N ^′ represent statistically significant widely used and newly proposed properties, respectively

The underline bold numbers indicate the highest values in each evaluation