Drug-Drug Interactions Prediction Using Fingerprint Only

Table 4

Comparison of fingerprint- and text-based classifiers under two types of tenfold cross-validation.

Cross-validation		Classifier	Model	Accuracy	Precision	Recall	F1-measure	MCC^a
Entire tenfold cross-validation		Fingerprint-based classifier (random forest)	Addition + subtraction	89.55%	92.25%	87.53%	89.83%	79.23%
		Text-based classifier (random forest)		84.16%	87.52%	82.01%	84.67%	68.48%
Composition tenfold cross-validation	ODIT^b test dataset	Fingerprint-based classifier (random forest)	Addition + Hadamard	80.01%	75.80%	82.81%	79.07%	60.33%
		Text-based classifier (random forest)		77.55%	72.92%	80.37%	76.42%	55.39%
	NDIT^c test dataset	Fingerprint-based classifier (random forest)	Addition + Hadamard	65.06%	42.58%	77.55%	54.49%	33.81%
		Text-based classifier (random forest)		66.97%	54.38%	72.80%	61.73%	35.26%

^aMathews correlation coefficient. ^bODIT: One Drug In Train set. ^cNDIT: No Drug In Train set.