CNN-Siam: multimodal siamese CNN-based deep learning approach for drug‒drug interaction prediction

BMC Bioinformatics

Table 1 Results of CNN-Siam and other models

Models	ACC	AUPR	AUC	F1	Precision	Recall
CNN-Siam	0.9237	0.9627	0.9986	0.9237	0.9237	0.9237
CNN-DDI*	0.8681	0.9254	0.9982	0.8681	0.8681	0.8681
CNN-DDI	0.8871	0.9251	0.9980	0.7496	0.8556	0.7220
DDIMDL	0.8852	0.9208	0.9976	0.7585	0.8471	0.7182
DeepDDI	0.8371	0.8899	0.9961	0.6848	0.7275	0.6611
DNN	0.8797	0.9134	0.9963	0.7223	0.8047	0.7027
RF	0.7775	0.8349	0.9956	0.5936	0.7893	0.5161
KNN	0.7214	0.7716	0.9813	0.4831	0.7174	0.4081
LR	0.7920	0.8400	0.9960	0.5948	0.7437	0.5236

A single * indicates the result of our implemented CNN-DDI run on the same dataset. The bold fonts indicate the best results

ISSN: 1471-2105