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Table 3 Class-wise performance of ngLOC method on eukaryotic datasets

From: ngLOC: software and web server for predicting protein subcellular localization in prokaryotes and eukaryotes

   Animal Plant
Localization class Code Prec. Sens. Spec. MCC Prec. Sens. Spec. MCC
Cytoplasm CYT 0.818 0.750 0.983 0.762 0.864 0.832 0.991 0.838
Cytoskeleton CSK 0.937 0.784 0.998 0.853 0.988 0.965 1.000 0.976
Endoplasmic Reticulum END 0.970 0.785 0.999 0.869 0.876 0.645 0.999 0.748
Extracellular EXC 0.953 0.946 0.974 0.922 0.966 0.723 0.999 0.831
Golgi Apparatus GOL 0.940 0.593 1.000 0.745 1.000 0.509 1.000 0.712
Lysosome LYS 0.949 0.693 1.000 0.810     
Mitochondria MIT 0.979 0.852 0.998 0.906 0.912 0.727 0.995 0.804
Nuclear NUC 0.805 0.914 0.960 0.831 0.769 0.873 0.976 0.802
Plasma Membrane PLA 0.876 0.957 0.961 0.890 0.796 0.866 0.989 0.822
Peroxisome POX 0.946 0.760 1.000 0.847 0.906 0.580 1.000 0.724
Cell Junction JNC 0.774 0.387 1.000 0.547     
Chloroplast CHL      0.946 0.977 0.899 0.889
Vacuole VAC      0.844 0.702 0.998 0.766
% Overall accuracy      89.88%     91.39%
  1. Prec-precision; Sens-sensitivity; Spec-specificity; MCC-Matthews correlation coefficient.