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Table 1 The improvement of the number of high quality sequence clusters in each information granule

From: Protein local 3D structure prediction by Super Granule Support Vector Machines (Super GSVM)

  G0 G1 G2 G3 G4 G5 G6 G7 G8 G9 Total
Total number of clusters 151 76 95 72 70 133 143 5 48 6 799
Original FGK-250
60%~70% 36 24 24 28 32 31 35 2 15 4 231
70%~80% 21 3 12 4 4 24 20 0 0 0 88
>80% 7 0 7 0 0 4 6 0 0 0 24
Super GSVM
60%~70% 44 30 31 30 42 39 40 3 24 4 287
70%~80% 27 17 17 16 16 27 30 1 4 1 156
>80% 26 2 19 2 1 26 23 0 0 1 100
  1. In this work, we use Super GSVM to generate and extract sequence clusters. We divided the whole training dataset into 10 information granules, the second row shows the number of clusters in each information granule (detail of this information can be found in [11]). The table also show the number of clusters belong to excellent (>80% 2nd structural similarity), good (70%~80% 2nd structural similarity), and fair (60%~70% 2nd structural similarity) clusters before and after the Super GSVM extraction.