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Table 2 The detailed prediction results of different modules and comparison of performance with BaCelLo method on non-redundant and organism specific datasets

From: ESLpred2: improved method for predicting subcellular localization of eukaryotic proteins

Datasets Localizations PSI-BLAST (A) (PSSM+AAC-NTerm) (B) Hybrid2 (A+B) Hybrid2 (10-fold CV) #Using BaCelLo strategy (B) BaCelLo 18 method
   ACC MCC ACC MCC ACC MCC ACC MCC ACC ACC (Third level)
Fungi dataset Cytoplasm 10.9 ---- 53.6 0.32 54.0 0.36 51.7 0.37 62.6 60.2
  Mitochondria 12.2 ---- 84.0 0.75 82.5 0.77 83.5 0.77 90.4 81.4
  Nuclear 39.7 ---- 73.0 0.73 78.6 0.59 80.7 0.60 74.7 67.1
  Extracellular 29.6 ---- 92.1 0.92 92.1 0.93 93.2 0.93 94.3 94.3
  Overall 29.5 ---- *72.7 0.56 75.9 0.60 77.0 0.61 80.5 70.1
  Average 23.1 ---- *75.7 0.63 76.8 0.66 77.3 0.67 76.5 75.8
   ACC MCC ACC MCC ACC MCC ACC MCC ACC ACC (Third level)
Animal dataset Cytoplasm 28.7 ---- 62.9 0.42 63.3 0.49 61.3 0.48 70.6 65.3
  Mitochondria 17.0 ---- 77.1 0.75 78.2 0.77 78.7 0.77 91.5 76.1
  Nuclear 53.8 ---- 69.0 0.60 77.7 0.68 79.1 0.69 72.6 64.8
  Extracellular 40.9 ---- 92.4 0.86 95.3 0.90 95.0 0.90 93.8 90.8
  Overall 42.9 ---- *75.8 0.66 80.8 0.72 81.0 0.73 80.1 73.8
  Average 35.0 ---- *75.4 0.66 78.6 0.71 78.5 0.71 82.1 74.2
   ACC MCC ACC MCC ACC MCC ACC MCC ACC ACC (Fourth level)
Plant dataset Chloroplast 31.4 ---- 77.5 0.67 81.9 0.69 82.8 0.71 90.7 73.0
  Cytoplasm 6.90 ---- 51.7 0.50 50.0 0.53 50.0 0.50 79.3 51.7
  Mitochondria 16.4 ---- 67.2 0.66 65.8 0.63 70.2 0.66 67.2 50.7
  Nuclear 48.8 ---- 80.2 0.77 81.8 0.76 81.8 0.79 86.8 71.9
  Extracellular 26.8 ---- 87.8 0.65 90.2 0.70 95.1 0.76 85.4 85.4
  Overall 30.3 ---- *74.5 0.66 76.6 0.68 78.0 0.70 84.7 68.2
  Average 26.4 ---- *72.9 0.64 73.9 0.67 76.0 0.69 81.9 66.6
  1. ACC is accuracy; MCC is Matthew correlation coefficient; ACC is calculated in percentage
  2. *Overall and average accuracy obtained at SVM parameters: For Fungi dataset (kernel = RBF, γ = 5, C = 4); Animal dataset (kernel = RBF, γ = 5, C = 2); Plant dataset (RBF, γ = 9, C = 3).
  3. # SVM parameters obtained for each class using hybrid1 features-For Fungi dataset (Cytoplasm: j = 4, γ = 7, C = 0.4, threshold value = 0.0; Mitochondria: j = 5, γ = 1, C = 1.6, threshold value = 0.0; Nuclear: j = 4, γ = 7, C = 0.54, threshold value = 0.0; Extracellular: j = 3, γ = 1, C = 1, threshold value = 0.0), Animal dataset (Cytoplasm: j = 3, γ = 9, C = 0.5, threshold value = 0.0; Mitochondria: j = 25, γ = 1, C = 2, threshold value = 0.0; Nuclear: j = 3, γ = 9, C = 0.5, threshold value = 0.0; Extracellular: j = 6, γ = 2, C = 1, threshold value = -0.1), Plant dataset (Cytoplasm: j = 2, γ = 3, C = 0.7, threshold value = 0.1; Mitochondria: j = 1, γ = 5, C = 75, threshold value = 0.2; Nuclear: j = 2, γ = 3, C = 0.7, threshold value = 0.1; Extracellular: j = 9, γ = 1, C = 1, threshold value = 0.0)