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Table 2 Performance comparison between fuzzy k-NN and k-NN models in three measures

From: A method to improve protein subcellular localization prediction by integrating various biological data sources

  ISORT (1-N) ISORT (2-NN) ISORT (3-NN) Fuzzy K-NN (k = 25, m = 1.05)
Measure I (k = 1) (%) 50.68 55.41 56.91 62.25
Measure I (k = 2) (%) 59.67 68.85 70.40 79.77
Measure I (k = 3) (%) 60.23 72.93 76.96 86.14
Measure II (%) 47.83 55.73 58.63 63.52
   Mitochondrion 43.81 28.43 38.13 35.12
   Vacuole 30.26 26.32 26.32 31.58
   Spindle pole 27.78 16.67 22.22 38.89
   Cell periphery 26.98 31.75 30.16 34.92
   Punctate composite 6.56 4.92 3.28 19.67
   Vacuolar membrane 8.11 10.81 0 8.11
   ER 41.61 44.97 41.61 53.02
   Nuclear periphery 50.00 35.00 45.00 50.00
   Endosome 40.74 40.74 40.74 40.74
   Bud neck 36.11 30.56 33.33 36.11
   Microtubule 45.46 45.46 45.46 45.46
   Golgi 28.57 28.57 23.81 23.81
   Late Golgi 21.74 13.04 17.39 21.74
   Peroxisome 33.33 33.33 33.33 33.33
   Actin 52.94 23.53 23.53 52.94
   Nucleolus 13.92 15.19 20.25 32.91
   Cytoplasm 49.08 64.72 66.18 79.88
   ER to Golgi 100.00 100.00 100.00 100.00
   Early Golgi 20.00 30.00 33.33 26.67
   Lipid particle 18.18 9.09 27.27 9.09
   Nucleus 63.47 78.03 83.25 77.91
   Bud 76.92 53.85 23.08 7.69
Measure III (%) 37.98 34.77 35.35 39.07