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Table 2 Prediction results

From: Machine learning on normalized protein sequences

Drug

linear max most

splines max most

fmm max most

periodic max most

natural max most

APV

0.934 ± 0.001

0.929 ± 0.002

0.928 ± 0.001

0.927 ± 0.001

0.928 ± 0.001

 

0.932 ± 0.001

0.934 ± 0.001

0.932 ± 0.002

0.933 ± 0.001

0.933 ± 0.001

ATV

0.936 ± 0.002

0.917 ± 0.003

0.920 ± 0.002

0.919 ± 0.002

0.920 ± 0.002

 

0.928 ± 0.002

0.915 ± 0.003

0.919 ± 0.003

0.918 ± 0.003

0.920 ± 0.003

IDV

0.972 ± 0.001

0.968 ± 0.001

0.968 ± 0.001

0.968 ± 0.001

0.968 ± 0.001

 

0.970 ± 0.001

0.970 ± 0.001

0.971 ± 0.001

0.971 ± 0.001

0.972 ± 0.001

LPV

0.964 ± 0.001

0.963 ± 0.001

0.963 ± 0.001

0.962 ± 0.001

0.963 ± 0.001

 

0.963 ± 0.001

0.964 ± 0.001

0.963 ± 0.001

0.963 ± 0.001

0.964 ± 0.001

NFV

0.941 ± 0.001

0.938 ± 0.001

0.940 ± 0.001

0.940 ± 0.001

0.940 ± 0.001

 

0.939 ± 0.001

0.943 ± 0.001

0.947 ± 0.001

0.946 ± 0.001

0.945 ± 0.001

RTV

0.984 ± 0.001

0.980 ± 0.001

0.981 ± 0.001

0.981 ± 0.001

0.981 ± 0.001

 

0.983 ± 0.001

0.986 ± 0.001

0.986 ± 0.001

0.986 ± 0.001

0.986 ± 0.001

SQV

0.955 ± 0.001

0.950 ± 0.001

0.951 ± 0.001

0.951 ± 0.001

0.951 ± 0.001

 

0.952 ± 0.001

0.953 ± 0.001

0.957 ± 0.001

0.955 ± 0.001

0.956 ± 0.001

3TC

0.933 ± 0.002

0.936 ± 0.002

0.939 ± 0.002

0.938 ± 0.002

0.939 ± 0.002

 

0.927 ± 0.003

0.934 ± 0.002

0.937 ± 0.002

0.937 ± 0.002

0.937 ± 0.003

ABC

0.916 ± 0.002

0.906 ± 0.002

0.909 ± 0.003

0.909 ± 0.002

0.909 ± 0.002

 

0.914 ± 0.003

0.910 ± 0.003

0.918 ± 0.003

0.919 ± 0.002

0.918 ± 0.003

AZT

0.908 ± 0.002

0.890 ± 0.002

0.894 ± 0.002

0.893 ± 0.002

0.894 ± 0.002

 

0.908 ± 0.002

0.898 ± 0.002

0.905 ± 0.002

0.903 ± 0.002

0.904 ± 0.002

d4T

0.903 ± 0.002

0.886 ± 0.002

0.889 ± 0.002

0.889 ± 0.002

0.889 ± 0.002

 

0.900 ± 0.002

0.892 ± 0.002

0.901 ± 0.002

0.899 ± 0.002

0.901 ± 0.002

ddI

0.853 ± 0.003

0.829 ± 0.003

0.837 ± 0.003

0.836 ± 0.003

0.836 ± 0.002

 

0.852 ± 0.003

0.841 ± 0.003

0.846 ± 0.003

0.839 ± 0.003

0.844 ± 0.003

TDF

0.832 ± 0.004

0.808 ± 0.005

0.817 ± 0.004

0.818 ± 0.005

0.816 ± 0.005

 

0.825 ± 0.005

0.812 ± 0.005

0.813 ± 0.005

0.814 ± 0.005

0.813 ± 0.005

DLV

0.901 ± 0.002

0.888 ± 0.002

0.891 ± 0.002

0.891 ± 0.002

0.891 ± 0.002

 

0.898 ± 0.002

0.881 ± 0.002

0.882 ± 0.002

0.883 ± 0.002

0.883 ± 0.002

EFV

0.932 ± 0.002

0.921 ± 0.002

0.928 ± 0.002

0.929 ± 0.002

0.928 ± 0.002

 

0.925 ± 0.002

0.911 ± 0.002

0.915 ± 0.002

0.919 ± 0.002

0.915 ± 0.002

NVP

0.917 ± 0.002

0.910 ± 0.002

0.916 ± 0.002

0.917 ± 0.002

0.916 ± 0.002

 

0.908 ± 0.003

0.902 ± 0.003

0.906 ± 0.003

0.909 ± 0.003

0.906 ± 0.003

BVM

0.918 ± 0.002

0.932 ± 0.002

0.932 ± 0.002

0.923 ± 0.003

0.933 ± 0.002

GTP

0.981 ± 0.001

0.979 ± 0.001

0.978 ± 0.001

0.977 ± 0.001

0.979 ± 0.001

 

0.980 ± 0.001

0.979 ± 0.001

0.979 ± 0.001

0.976 ± 0.001

0.979 ± 0.001

MIP

0.815 ± 0.010

0.789 ± 0.013

0.789 ± 0.011

0.787 ± 0.016

0.788 ± 0.017

 

0.827 ± 0.012

0.815 ± 0.014

0.813 ± 0.014

0.816 ± 0.013

0.812 ± 0.013

  1. AUC ± standard deviations with max representing the maximal occuring sequence length within a dataset, most the most frequent sequence length in a dataset. For BVM most and max are the same.