Table 1. Accuracy tests on 25 datasets.

	RBscore			SVM			aaRNA			RNABindRPlus			BindN+_RNA			BindN+_DNA
Cutoff = 3.5 Å	wAUC	mAUC	tAUC	wAUC	mAUC	tAUC	wAUC	mAUC	tAUC	wAUC	mAUC	tAUC	wAUC	mAUC	tAUC	wAUC	mAUC	tAUC
BindN_R107	0.850	0.847	0.866	0.878	0.898	0.943	0.827	0.828	0.877	0.907	0.884	0.936	0.897	0.893	0.925	0.761	0.765	0.815
PPRInt_R86	0.857	0.855	0.863	0.884	0.910	0.947	0.835	0.835	0.883	0.918	0.909	0.946	0.871	0.876	0.913	0.771	0.777	0.810
RNABindR_R144	0.860	0.849	0.868	0.849	0.865	0.921	0.828	0.827	0.877	0.894	0.865	0.922	0.819	0.828	0.882	0.737	0.753	0.801
RNABindR_R147	0.860	0.848	0.868	0.849	0.865	0.922	0.828	0.826	0.877	0.894	0.865	0.922	0.819	0.828	0.883	0.737	0.752	0.802
RNABindR_R44	0.862	0.863	0.869	0.810	0.819	0.844	0.817	0.822	0.845	0.763	0.770	0.800	0.784	0.792	0.822	0.754	0.764	0.790
RNABindR_R111	0.898	0.869	0.867	0.789	0.787	0.839	0.849	0.825	0.842	0.762	0.739	0.740	0.768	0.755	0.780	0.748	0.733	0.767
meta2_R44	0.862	0.863	0.869	0.810	0.819	0.844	0.817	0.822	0.845	0.763	0.770	0.800	0.784	0.792	0.822	0.754	0.764	0.790
aaRNA_R67	0.857	0.857	0.874	0.753	0.777	0.815	0.814	0.812	0.846	0.757	0.755	0.776	0.764	0.777	0.810	0.738	0.744	0.783
aaRNA_R141	0.875	0.858	0.836	0.814	0.811	0.848	0.834	0.820	0.835	0.846	0.836	0.854	0.780	0.781	0.792	0.736	0.739	0.731
aaRNA_R205	0.877	0.864	0.867	0.836	0.854	0.911	0.841	0.837	0.878	0.847	0.834	0.881	0.795	0.808	0.853	0.748	0.759	0.792
RBscore_R130	0.886	0.870	0.864	0.947	0.947	0.969	0.838	0.832	0.877	0.828	0.822	0.871	0.806	0.821	0.867	0.759	0.765	0.801
RBscore_R117	0.867	0.855	0.843	0.719	0.723	0.774	0.829	0.820	0.852	0.798	0.789	0.826	0.743	0.747	0.783	0.723	0.728	0.754
Sungwook_R267	0.865	0.848	0.837	0.874	0.865	0.886	0.830	0.811	0.824	0.808	0.796	0.808	0.797	0.783	0.815	0.744	0.741	0.742
Sungwook_R727	0.867	0.857	0.881	0.893	0.894	0.935	0.839	0.833	0.879	0.827	0.820	0.869	0.821	0.822	0.874	0.768	0.773	0.824
BindN_D62	0.909	0.897	0.864	0.776	0.787	0.787	0.881	0.875	0.862	0.787	0.788	0.786	0.822	0.826	0.838	0.944	0.938	0.944
Shandar_D140	0.900	0.893	0.878	0.765	0.772	0.780	0.852	0.855	0.857	0.778	0.778	0.760	0.820	0.821	0.832	0.834	0.855	0.852
Susan_D56	0.918	0.912	0.890	0.766	0.776	0.775	0.872	0.870	0.867	0.766	0.774	0.759	0.808	0.814	0.823	0.843	0.872	0.860
DBindR_D374	0.895	0.887	0.874	0.748	0.757	0.780	0.856	0.856	0.862	0.772	0.777	0.768	0.795	0.807	0.823	0.813	0.840	0.843
DISPLAR_D428	0.894	0.885	0.869	0.757	0.764	0.774	0.854	0.853	0.860	0.775	0.780	0.771	0.803	0.812	0.825	0.824	0.846	0.847
DNABINDPROT_D54	0.920	0.903	0.867	0.723	0.736	0.756	0.864	0.851	0.853	0.754	0.745	0.700	0.786	0.790	0.793	0.807	0.827	0.818
PreDNA_D224	0.896	0.889	0.873	0.748	0.747	0.759	0.857	0.857	0.859	0.764	0.768	0.758	0.794	0.800	0.812	0.802	0.819	0.823
RBscore_D381	0.895	0.884	0.875	0.748	0.742	0.758	0.852	0.844	0.854	0.761	0.764	0.768	0.797	0.795	0.806	0.796	0.805	0.810
metaDBSite_D232	0.898	0.889	0.872	0.753	0.744	0.761	0.858	0.856	0.858	0.769	0.769	0.763	0.798	0.799	0.812	0.804	0.818	0.821
metaDBSite_D316	0.898	0.885	0.878	0.755	0.756	0.772	0.853	0.851	0.859	0.770	0.770	0.769	0.799	0.802	0.818	0.809	0.829	0.834
SDCPred_D159	0.902	0.896	0.880	0.760	0.769	0.773	0.854	0.857	0.856	0.772	0.772	0.753	0.812	0.814	0.823	0.827	0.848	0.843

Datasets after BindN_D62 are DBP datasets. RBscore_R130 is the training set of RBscore and the SVM approach. See Materials and Methods for descriptions of the dataset.