Table 2.

Classification performance of XGBoost, DNN, and MOMA in ROSMAP NL and AD classifications, TCGA 34 classes classification, and TCGA early- and late-stage classification

		XGBoost				XGBoost				DNN				DNN				MOMA
		GE				DM				GE				DM				GEa				DMa
Task and datasets		ACC	F1	AUC	MCC	ACC	F1	AUC	MCC	ACC	F1	AUC	MCC	ACC	F1	AUC	MCC	ACC	F1	AUC	MCC	ACC	F1	AUC	MCC
ROSMAP NL/AD		0.686	0.723	0.757	0.363	0.596	0.664	0.632	0.168	0.653	0.740	0.764	0.284	0.589	0.581	0.616	0.180	0.737	0.740	0.812	0.488	0.720	0.753	0.807	0.432
TCGA 34 classes		0.953	0.951	0.998	0.951	0.950	0.949	0.998	0.948	0.878	0.848	0.988	0.874	0.875	0.837	0.985	0.870	0.948	0.945	0.996	0.946	0.954	0.952	0.996	0.952
TCGA early- and late-stage	ACC	0.633	0.455	0.681	0.191	0.672	0.435	0.673	0.241	0.618	0.386	0.671	0.157	0.578	0.305	0.710	0.024	0.723	0.654	0.725	0.429	0.710	0.611	0.720	0.393
	BLCA	0.756	0.832	0.772	0.410	0.685	0.785	0.688	0.221	0.675	0.806	0.636	0.000	0.699	0.800	0.712	0.223	0.705	0.771	0.736	0.352	0.663	0.716	0.747	0.335
	BRCA	0.718	0.179	0.630	0.101	0.716	0.154	0.584	0.094	0.701	0.188	0.573	0.088	0.705	0.232	0.602	0.117	0.692	0.288	0.616	0.134	0.651	0.331	0.618	0.125
	COAD	0.634	0.539	0.664	0.247	0.605	0.499	0.627	0.185	0.579	0.513	0.626	0.178	0.609	0.367	0.644	0.179	0.630	0.624	0.703	0.310	0.667	0.605	0.726	0.322
	ESCA	0.547	0.369	0.508	0.033	0.548	0.319	0.457	0.013	0.479	0.347	0.472	0.000	0.566	0.218	0.542	0.000	0.509	0.467	0.526	0.026	0.510	0.417	0.441	−0.001
	HNSC	0.780	0.874	0.607	0.131	0.605	0.499	0.627	0.185	0.778	0.875	0.542	0.000	0.778	0.875	0.501	0.000	0.728	0.832	0.631	0.079	0.648	0.727	0.643	0.196
	KICH	0.631	0.280	0.617	0.088	0.646	0.253	0.567	0.062	0.692	0.067	0.522	0.000	0.554	0.201	0.450	0.018	0.631	0.357	0.556	0.141	0.708	0.286	0.617	0.176
	KIRC	0.719	0.647	0.781	0.421	0.735	0.662	0.798	0.453	0.719	0.641	0.772	0.476	0.735	0.584	0.752	0.429	0.754	0.717	0.836	0.507	0.754	0.714	0.830	0.510
	KIRP	0.845	0.638	0.845	0.573	0.833	0.592	0.848	0.523	0.817	0.463	0.819	0.430	0.805	0.447	0.789	0.400	0.813	0.637	0.842	0.522	0.849	0.684	0.858	0.602
	LIHC	0.725	0.226	0.593	0.120	0.696	0.127	0.644	−0.004	0.733	0.088	0.547	0.034	0.733	0.236	0.665	0.134	0.699	0.366	0.680	0.188	0.730	0.314	0.688	0.190
	LUAD	0.779	0.056	0.605	0.020	0.792	0.130	0.643	0.132	0.794	0.000	0.612	0.000	0.794	0.000	0.509	0.000	0.711	0.241	0.595	0.092	0.761	0.177	0.612	0.092
	LUSC	0.834	0.029	0.564	0.020	0.831	0.084	0.460	0.085	0.703	0.057	0.567	0.000	0.837	0.000	0.533	0.057	0.698	0.190	0.506	0.023	0.695	0.224	0.500	0.044
	MESO	0.758	0.837	0.682	0.401	0.610	0.740	0.511	−0.027	0.701	0.822	0.622	0.033	0.690	0.816	0.421	−0.032	0.643	0.773	0.572	−0.064	0.609	0.719	0.607	0.019
	READ	0.571	0.624	0.519	0.138	0.536	0.560	0.582	0.063	0.523	0.620	0.605	−0.007	0.501	0.415	0.521	0.000	0.410	0.473	0.461	−0.203	0.465	0.516	0.479	−0.121
	SKCM	0.622	0.160	0.457	−0.059	0.623	0.237	0.590	0.027	.614	0.092	0.528	0.000	0.684	0.000	0.382	−0.030	0.654	0.292	0.490	0.101	0.644	0.153	0.446	0.001
	STAD	0.550	0.582	0.592	0.096	0.569	0.617	0.595	0.132	0.581	0.579	0.626	0.173	0.500	0.301	0.541	0.035	0.594	0.637	0.627	0.183	0.594	0.587	0.643	0.198
	THCA	0.719	0.460	0.718	0.307	0.731	0.487	0.732	0.343	0.705	0.350	0.694	0.262	0.735	0.434	0.708	0.322	0.687	0.595	0.761	0.378	0.727	0.618	0.769	0.418
	UVM	0.378	0.358	0.404	−0.252	0.480	0.454	0.509	−0.038	0.506	0.592	0.608	0.013	0.493	0.267	0.469	0.000	0.533	0.501	0.587	0.060	0.545	0.417	0.580	0.115

Note: The performance of the methods was evaluated for gene expression (GE) and DNA methylation (DM) datasets. Bold texts indicate the best performance in each metric, ACC, F1-score, AUC and MCC.

^aThe dataset used for the performance measurement (a task data set) is specified.