DIRProt: a computational approach for discriminating insecticide resistant proteins from non-resistant proteins

Meher, Prabina Kumar; Sahu, Tanmaya Kumar; Banchariya, Anjali; Rao, Atmakuri Ramakrishna

doi:10.1186/s12859-017-1587-y

BMC Bioinformatics

Table 1 Estimates of different performance metrics for SVM with RBF kernel in discriminating resistant from non-resistant proteins, under all the feature sets as well as different percentage of sequence identity in the positive dataset

From: DIRProt: a computational approach for discriminating insecticide resistant proteins from non-resistant proteins

		Performance metrics
Id(%)	Feature	Sn	Sp	Ac	Pre	MCC	AUC-ROC
40	AAC	0.836 ± 0.018	0.952 ± 0.014	0.894 ± 0.012	0.946 ± 0.015	0.794 ± 0.024	0.924 ± 0.020
	DPC	0.849 ± 0.013	0.983 ± 0.011	0.916 ± 0.009	0.980 ± 0.012	0.839 ± 0.017	0.948 ± 0.011
	PAAC	0.836 ± 0.018	0.956 ± 0.014	0.896 ± 0.013	0.951 ± 0.015	0.798 ± 0.026	0.922 ± 0.018
	CTD	0.841 ± 0.015	0.981 ± 0.011	0.911 ± 0.010	0.978 ± 0.013	0.831 ± 0.020	0.932 ± 0.010
	ACF	0.836 ± 0.017	0.9530.016	0.895 ± 0.012	0.947 ± 0.017	0.795 ± 0.025	0.901 ± 0.017
60	AAC	0.870 ± 0.012	0.959 ± 0.008	0.914 ± 0.008	0.955 ± 0.009	0.832 ± 0.016	0.946 ± 0.008
	DPC	0.875 ± 0.008	0.986 ± 0.007	0.931 ± 0.006	0.984 ± 0.007	0.866 ± 0.011	0.972 ± 0.005
	PAAC	0.870 ± 0.014	0.960 ± 0.010	0.915 ± 0.010	0.956 ± 0.011	0.833 ± 0.020	0.947 ± 0.010
	CTD	0.860 ± 0.011	0.985 ± 0.007	0.923 ± 0.007	0.983 ± 0.008	0.852 ± 0.014	0.959 ± 0.006
	ACF	0.869 ± 0.011	0.964 ± 0.009	0.917 ± 0.007	0.960 ± 0.009	0.837 ± 0.015	0.932 ± 0.009
70	AAC	0.886 ± 0.011	0.961 ± 0.008	0.924 ± 0.008	0.958 ± 0.008	0.850 ± 0.015	0.953 ± 0.008
	DPC	0.883 ± 0.008	0.987 ± 0.005	0.935 ± 0.005	0.986 ± 0.005	0.875 ± 0.009	0.973 ± 0.004
	PAAC	0.891 ± 0.010	0.961 ± 0.008	0.926 ± 0.007	0.958 ± 0.008	0.854 ± 0.013	0.955 ± 0.007
	CTD	0.866 ± 0.010	0.987 ± 0.005	0.926 ± 0.006	0.985 ± 0.006	0.859 ± 0.012	0.961 ± 0.006
	ACF	0.888 ± 0.008	0.963 ± 0.009	0.925 ± 0.006	0.960 ± 0.009	0.853 ± 0.013	0.948 ± 0.007
90	AAC	0.886 ± 0.010	0.959 ± 0.006	0.923 ± 0.006	0.956 ± 0.006	0.847 ± 0.012	0.955 ± 0.006
	DPC	0.899 ± 0.009	0.989 ± 0.005	0.944 ± 0.006	0.988 ± 0.005	0.892 ± 0.011	0.978 ± 0.004
	PAAC	0.889 ± 0.011	0.959 ± 0.007	0.924 ± 0.007	0.956 ± 0.007	0.850 ± 0.014	0.956 ± 0.006
	CTD	0.887 ± 0.008	0.987 ± 0.005	0.937 ± 0.005	0.985 ± 0.006	0.878 ± 0.010	0.972 ± 0.005
	ACF	0.894 ± 0.010	0.967 ± 0.006	0.930 ± 0.006	0.964 ± 0.006	0.863 ± 0.013	0.949 ± 0.006

Id(%): maximum percentage of pair-wise sequence identity present in the positive dataset
Sn Sensitivity, Sp Specificity, Ac Accuracy, Pre Precision, MCC Matthew’s correlation coefficient, AUC-ROC area under ROC curves

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