HybridGCN for protein solubility prediction with adaptive weighting of multiple features

Table 5 Performance comparisons with SOTA methods

Methods	RMSE	\(R^2\)	Accuracy	Precision	Recall	F1	AUC
K-nearest Neighbor	0.284	0.214	0.691	0.737	0.486	0.586	0.776
Linear Regression	0.280	0.240	0.707	0.685	0.642	0.663	0.777
Random Forest	0.255	0.370	0.760	0.750	0.690	0.729	0.825
XGboost	0.252	0.385	0.756	0.748	0.690	0.718	0.829
LSTM	0.236	0.458	0.765	0.748	0.677	0.730	0.855
SVM	0.246	0.411	0.761	0.763	0.684	0.721	0.842
ProteinSol	0.253	0.376	0.714	0.689	0.688	0.693	0.808
DeepSol	0.241	0.434	0.763	0.771	0.738	0.695	0.845
ProGAN	0.237	0.442	0.763	0.770	0.676	0.720	0.853
SeqVec	0.236	0.458	0.767	0.754	0.715	0.734	0.858
TAPE	0.235	0.461	0.764	0.774	0.710	0.730	0.856
NetSolP	0.240	0.449	0.760	0.768	0.716	0.722	0.833
GraphSOLSingle	0.231	0.483	0.779	0.775	0.693	0.732	0.866
GraphSOLEnsemble	0.227	0.501	0.782	0.790	0.702	0.743	0.873
OursSingle	0.227	0.497	0.783	0.780	0.722	0.749	0.876
OursEnsemble	0.226	0.510	0.801	0.816	0.729	0.764	0.886

ISSN: 1758-2946