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Table 2 Performance of centrality subsets

From: Model-based clustering reveals vitamin D dependent multi-centrality hubs in a network of vitamin-related proteins

  MBCs1 sizes1 MBCs2 1 cluster 2 clusters
D-EC-TI1-TI4 VEV (19) 102 VVV (3) 11 (12) 19 (52)
TI1-TI4-B-C VEV (16) 132 VVV (4) 14 (17) 22 (55)
EC-TI1-TI4-B VEV (13) 182 VVV (4) 14 (19) 22 (68)
D-TI1-TI4-C VEV (10) 169 VVV (4) 16 (25) 22 (63)
6 centralities VEV (7) 140 VEV (6) 22
  1. Efficiency of different subsets of 4 centralities in identifying the group of 22 most central proteins extracted with the complete set of 6 indices. Topological importance up to 1 and 4 steps were always included, together with two other centralities listed in each row (e.g., subset of the first row = D, EC, TI1, TI4). MBC describes the optimal model obtained by clustering the proteins in the first (s1) and second (s2) step (number of clusters between parentheses). In the second column we summarized the size of the first protein cluster extracted. The number of the original 22 central proteins identified with one or two clusters are listed in the last columns (size of clusters between parentheses). VEV indicates ellipsoidal, equal shape model; VVV stands for ellipsoidal, varying volume, shape, and orientation model. The last row summarizes results obtained with the whole set of 6 centralities.