BMC Bioinformatics

Table 3 Performance of models based on both positive dataset and negative 1 dataset using SMO as classifier

From: Incorporating support vector machine with sequential minimal optimization to identify anticancer peptides

Features	Training				Testing
Features	Sensitivity	Specificity	Accuracy	MCC	Sensitivity	Specificity	Accuracy	MCC
AAC	0.756	0.888	0.822	0.587	0.760	0.840	0.800	0.556
N5C5	0.700	0.808	0.754	0.511	0.660	0.820	0.740	0.486
k-space = 0	0.790	0.838	0.814	0.629	0.830	0.860	0.845	0.690
k-space = 1	0.834	0.868	0.851	0.702	0.830	0.860	0.845	0.690
k-space = 2	0.812	0.877	0.844	0.690	0.770	0.800	0.785	0.570
AAC + k-space = 0	0.840	0.793	0.816	0.634	0.850	0.860	0.855	0.710
AAC + N5C5	0.728	0.873	0.800	0.607	0.720	0.860	0.790	0.586
N5C5 + k-space = 0	0.784	0.834	0.809	0.618	0.820	0.840	0.830	0.660
AAC + N5C5 + k-space 0	0.793	0.849	0.821	0.642	0.830	0.860	0.845	0.690
PSSM	0.844	0.862	0.853	0.706	0.850	0.800	0.825	0.651

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ISSN: 1471-2105

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