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Table 1 Performance with individual and all data sources

From: Enhanced protein fold recognition through a novel data integration approach

Data sources MKLdiv-dc MKLdiv-conv SimpleMKL VBKC MKL-RKDA
Amino acid composition (C) 51.69 51.69 51.83 51.2 ± 0.5 45.43
Predicted secondary structure (S) 40.99 40.99 40.73 38.1 ± 0.3 38.64
Hypdrophobicity (H) 36.55 36.55 36.55 32.5 ± 0.4 34.20
Polarity (P) 35.50 35.50 35.50 32.2 ± 0.3 30.54
van der Walls volume (V) 37.07 37.07 37.85 32.8 ± 0.3 30.54
Polarizability (Z) 37.33 37.33 36.81 33.2 ± 0.4 30.28
PseAA λ = 1 (L1) 44.64 44.64 45.16 41.5 ± 0.5 36.55
PseAA λ = 4 (L4) 44.90 44.90 44.90 41.5 ± 0.4 38.12
PseAA λ = 14 (L14) 43.34 43.34 43.34 38 ± 0.2 40.99
PseAA λ = 30 (L30) 31.59 31.59 31.59 32 ± 0.2 36.03
SW with BLOSUM62 (SW1) 62.92 62.92 62.40 59.8 ± 1.9 61.87
SW with PAM50 (SW2) 63.96 63.96 63.44 49 ± 0.7 64.49
All data sources 73.36 71.01 66.57 68.1 ± 1.2 68.40
Uniform weighted 68.40 68.40 68.14 - 66.06
  1. The results of VBKC are cited from [21]. The results not employed there are denoted by '-'. The best result for each kernel learning method is marked in bold.