Table 3.

Classification performance of MORONET, MOFA+SVM, SMSPL, P-NET and MOMA in ROSMAP NL and AD classification, TCGA 34 classes classification and TCGA early- and late-stage classification

		MORONET					MOFA+SVM					SMSPL					P-NET					Ensem-MOMA
Task and datasets		ACC	F1	AUC	MCC	AP	ACC	F1	AUC	MCC	AP	ACC	F1	AUCa	MCC	AP	ACC	F1	AUC	MCC	AP	ACC	F1	AUC	MCC	AP
ROSMAP NL/AD		0.612	0.647	0.648	0.222	0.661	0.663	0.699	0.769	0.320	0.826	0.689	0.732	0.678	0.368	0.757	0.663	0.708	0.777	0.313	0.843	0.732	0.750	0.815	0.466	0.861
TCGA 34 classes		0.945	0.943	0.997	0.943	0.971	0.869	0.865	0.994	0.863	0.914	0.891	0.874	0.902	0.886	0.805	0.725	0.757	0.996	0.739	0.948	0.958	0.957	0.996	0.957	0.980
TCGA early- and late-stage	ACC	0.712	0.508	0.747	0.351	0.663	0.553	0.084	0.670	−0.087	0.541	0.592	0.413	0.557	0.115	0.458	0.659	0.465	0.777	0.228	0.706	0.723	0.663	0.730	0.438	0.596
	BLCA	0.682	0.755	0.669	0.305	0.772	0.712	0.803	0.713	0.291	0.821	0.717	0.804	0.638	0.305	0.840	0.685	0.775	0.715	0.253	0.842	0.712	0.787	0.742	0.342	0.852
	BRCA	0.654	0.246	0.567	0.056	0.319	0.728	0.000	0.525	0.000	0.310	0.663	0.265	0.548	0.082	0.299	0.708	0.366	0.611	0.193	0.408	0.634	0.405	0.620	0.168	0.419
	COAD	0.623	0.580	0.633	0.244	0.568	0.649	0.575	0.681	0.279	0.615	0.645	0.613	0.645	0.292	0.584	0.656	0.605	0.738	0.302	0.700	0.641	0.660	0.713	0.335	0.665
	ESCA	0.534	0.255	0.477	−0.032	0.454	0.567	0.188	0.434	0.018	0.411	0.585	0.395	0.547	0.110	0.416	0.554	0.393	0.488	0.055	0.462	0.484	0.459	0.489	−0.007	0.467
	HNSC	0.748	0.852	0.555	0.028	0.819	0.733	0.838	0.617	0.051	0.857	0.757	0.853	0.562	0.142	0.819	0.753	0.856	0.609	0.060	0.855	0.726	0.834	0.632	0.035	0.859
	KICH	0.646	0.397	0.567	0.195	0.488	0.708	0.080	0.461	0.087	0.451	0.662	0.214	0.533	0.068	0.371	0.708	0.080	0.561	0.087	0.529	0.662	0.337	0.583	0.152	0.534
	KIRC	0.689	0.565	0.732	0.350	0.656	0.735	0.657	0.766	0.450	0.696	0.751	0.691	0.740	0.492	0.722	0.744	0.690	0.797	0.473	0.716	0.751	0.714	0.832	0.503	0.768
	KIRP	0.764	0.515	0.784	0.388	0.578	0.801	0.426	0.853	0.378	0.733	0.846	0.648	0.758	0.572	0.666	0.809	0.574	0.853	0.463	0.738	0.837	0.682	0.850	0.584	0.763
	LIHC	0.699	0.352	0.571	0.175	0.384	0.707	0.164	0.646	0.065	0.384	0.681	0.191	0.522	0.018	0.317	0.684	0.298	0.657	0.106	0.381	0.684	0.452	0.685	0.239	0.418
	LUAD	0.765	0.127	0.481	0.050	0.252	0.794	0.000	0.629	0.000	0.330	0.740	0.135	0.506	0.011	0.240	0.774	0.150	0.609	0.091	0.324	0.714	0.287	0.598	0.119	0.320
	LUSC	0.814	0.050	0.477	0.008	0.214	0.837	0.000	0.577	0.000	0.238	0.784	0.083	0.497	−0.021	0.192	0.812	0.025	0.405	−0.036	0.188	0.715	0.212	0.504	0.048	0.236
	MESO	0.608	0.715	0.584	0.004	0.790	0.690	0.816	0.481	−0.032	0.731	0.622	0.736	0.521	0.036	0.750	0.642	0.768	0.562	−0.056	0.792	0.666	0.790	0.621	0.044	0.813
	READ	0.534	0.633	0.486	0.062	0.562	0.443	0.448	0.614	−0.121	0.673	0.594	0.578	0.598	0.210	0.574	0.522	0.593	0.488	0.024	0.592	0.523	0.592	0.473	0.046	0.606
	SKCM	0.602	0.194	0.565	−0.015	0.445	0.694	0.000	0.625	0.000	0.487	0.592	0.150	0.463	−0.095	0.262	0.603	0.057	0.433	−0.138	0.335	0.644	0.260	0.456	0.070	0.391
	STAD	0.550	0.502	0.577	0.127	0.601	0.564	0.610	0.570	0.120	0.601	0.597	0.626	0.595	0.192	0.642	0.611	0.650	0.629	0.217	0.647	0.586	0.630	0.636	0.166	0.656
	THCA	0.669	0.469	0.690	0.237	0.535	0.701	0.337	0.698	0.222	0.582	0.693	0.546	0.667	0.329	0.579	0.723	0.515	0.745	0.338	0.648	0.703	0.602	0.765	0.385	0.701
	UVM	0.457	0.477	0.521	−0.072	0.572	0.544	0.603	0.387	0.087	0.503	0.533	0.492	0.536	0.076	0.514	0.563	0.453	0.531	0.140	0.620	0.596	0.561	0.577	0.205	0.628

Note: Bold texts indicate the best performance in each metric, accuracy, F1-score, AUC, MCC and AP.

^aIn SMSPL, predictor is categorical. AUC is computed by categorical values.