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