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Table 7 The selected (best) SVM model for each protein class, i.e. the penalty C, the kernel function and its parameters (γ, d, r)

From: Exploring the potential of 3D Zernike descriptors and SVM for protein–protein interface prediction

Protein complex class Receptor ligand Bound unbound No. features kernel function C γ d r
A r b 109 sigmoid 495.33 0.00054 N/A 1.44470
   u 96 rbf 1365.14 0.00039 N/A N/A
  l b 89 linear 46.05 N/A N/A N/A
   u 85 linear 221.64 N/A N/A N/A
AB r b 117 poly 23.87 0.03006 2 1.73464
   u 108 poly 426.47 0.01110 3 0.01539
  l b 78 poly 2157.88 0.01906 7 0.17614
   u 75 poly 4362.45 0.03470 10 -0.03613
EI r b 105 poly 1514.50 0.00003 3 -0.15922
   u 129 sigmoid 33.32 0.00029 N/A -1.61953
  l b 91 sigmoid 213.15 0.00065 N/A 0.47294
   u 80 poly 1916.02 0.01531 4 0.13840
ER r b 115 rbf 9.22 0.00366 N/A N/A
   u 126 rbf 298.47 0.00222 N/A N/A
  l b 100 sigmoid 157.32 0.00024 N/A -0.24272
   u 100 poly 1001.44 0.00597 5 0.00039
ES r b 84 linear 196.85 N/A N/A N/A
   u 79 linear 7010.36 N/A N/A N/A
  l b 83 poly 954.76 0.00581 6 1.00104
   u 86 poly 721.43 0.02692 6 0.00022
OG r b 102 poly 8543.28 0.01682 6 0.00004
   u 107 rbf 12.42 0.00062 N/A N/A
  l b 92 poly 257.51 0.00575 3 -0.00191
   u 78 poly 3421.90 0.01659 8 0.00014
OR r b 97 linear 281.56 N/A N/A N/A
   u 68 linear 1804.59 N/A N/A N/A
  l b 79 poly 5502.26 0.01908 9 0.00113
   u 100 sigmoid 63.94 0.00261 N/A -1.90377
OX r b 141 rbf 60.29 0.00029 N/A N/A
   u 122 rbf 747.39 0.00006 N/A N/A
  l b 132 poly 383.62 0.02146 8 0.04259
   u 118 poly 779.96 0.02933 9 0.05214
Generic model b 83 sigmoid 148.639 0.02312 N/A -1.44779
  u 76 sigmoid 3218.238 0.00196 N/A 1.92731
  1. The “No. features” column indicates the number of selected features resulting from the Randomized Logistic Regression algorithm