Table 3.

Comparison of CNA_origin predictions with those of other algorithms.

Cancer	Predictor	Precision	Recall	F1-score
BRCA	CNA_origin	0.8750	0.9231	0.8984
	LSTM	0.8713	0.8462	0.8585
	RF	0.8556	0.8645	0.8601
	XGboost	0.8214	0.8846	0.8519
	CNA_zhang	0.7916	0.8735	0.8306
COADREAD	CNA_origin	0.8158	0.7381	0.7750
	LSTM	0.8571	0.8077	0.8317
	RF	0.7659	0.6923	0.7272
	XGboost	0.7959	0.7500	0.7723
	CNA_zhang	0.6000	0.7346	0.6605
GBM	CNA_origin	0.9310	0.8438	0.8852
	LSTM	0.8913	0.8913	0.8913
	RF	0.8627	0.8627	0.8627
	XGboost	0.9535	0.8913	0.9213
	CNA_zhang	0.8870	0.8593	0.8730
KIRC	CNA_origin	0.8889	0.9600	0.9231
	LSTM	0.8837	0.9268	0.9048
	RF	0.9056	0.8571	0.8807
	XGboost	0.8780	0.8780	0.8780
	CNA_zhang	0.8085	0.9268	0.8636
OV	CNA_origin	0.8980	0.8627	0.8800
	LSTM	0.7843	0.9091	0.8421
	RF	0.7826	0.9000	0.8372
	XGboost	0.7551	0.8409	0.7957
	CNA_zhang	0.8461	0.7586	0.8000
UCEC	CNA_origin	0.6792	0.7200	0.6990
	LSTM	0.6897	0.6557	0.6723
	RF	0.6451	0.6060	0.6250
	XGboost	0.7407	0.6557	0.6957
	CNA_zhang	0.7419	0.4693	0.5750

The bold values are the best performance among counterparts.