Table 2.

LOOCV performance of the individual models

Encoding	ML algorithm	Sensitivity	Specificity	Accuracy	MCC
AAC	OET-KNN	71.34	81.08	76.28	0.5271
	KNN	75.72	74.87	75.29	0.5058
	SVM	70.96	83.47	77.30	0.5492
	GBM	71.86	83.75	77.89	0.5606
	RF	68.11	85.13	76.73	0.5409
PseAAC	OET-KNN	73.05	81.38	77.27	0.5465
	KNN	74.24	79.38	76.84	0.5370
	SVM	70.59	83.98	77.37	0.5511
	GBM	74.99	86.07	80.60	0.6149
	RF	68.84	84.86	76.95	0.5446
PAAC	OET-KNN	68.94	72.09	70.53	0.4105
	KNN	72.96	66.26	69.57	0.3930
	SVM	76.15	84.22	80.24	0.6060
	GBM	71.33	85.01	77.84	0.5661
	RF	71.00	81.67	76.41	0.5301
SAAC	OET-KNN	66.63	72.88	69.80	0.3960
	KNN	69.75	68.81	69.28	0.3856
	SVM	72.51	85.85	79.27	0.5895
	GBM	73.90	85.95	80.00	0.6034
	RF	67.82	87.02	77.54	0.5595
Pse-PSSM, $\lambda =0$	OET-KNN	86.57	92.75	89.70	0.7953
	KNN	85.22	90.44	87.86	0.7580
	SVM	83.23	90.05	86.68	0.7350
	GBM	83.41	90.45	86.98	0.7409
	RF	79.45	92.53	86.08	0.7269
Pse-PSSM, $\lambda =1$	OET-KNN	85.92	91.79	88.89	0.7788
	KNN	85.89	89.06	87.50	0.7501
	SVM	86.75	92.22	89.52	0.7912
	GBM	85.00	92.19	88.64	0.7744
	RF	79.86	93.66	86.85	0.7433
Pse-PSSM, $\lambda =2$	OET-KNN	85.51	91.90	88.75	0.7762
	KNN	85.65	88.28	86.98	0.7397
	SVM	86.83	92.06	89.48	0.7904
	GBM	84.86	91.72	88.34	0.7682
	RF	79.80	93.70	86.84	0.7432

This table shows microaverage LOOCV performance of the different protein encodings on different machine learning algorithms. The SAAC with SVM, highlighted in italics, reflects the LOOCV performance of the iMem-2LSAAC method [3] on DS-M. Only the Pse-PSSMs where $\lambda \in (0,1,2)$ are shown here; the complete performance of all the Pse-PSSMs ($\lambda \in (0,\dots ,49))$ can be found in Additional file 1