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Table 1 The relationships between PeC and fifteen other centrality measures for predicting the top 100 proteins.

From: A new essential protein discovery method based on the integration of protein-protein interaction and gene expression data

Centrality measures (Mi) |PeCMi| |M i - PeC| Non-essential proteins in {M i - PeC} Percentage of non-essential proteins in {M i - PeC} with low PeC
Degree Centrality (DC) 18 82 44 54.5%
Betweenness Centrality (BC) 16 84 47 51.1%
Closeness Centrality (CC) 16 84 51 56.9%
Subgraph Centrality(SC) 11 89 59 64.4%
Eigenvector Centrality(EC) 11 89 59 64.4%
Information Centrality(IC) 17 83 47 55.3%
Bottle Neck (BN) 16 84 53 45.3%
Density of Maximum Neighborhood Component (DMNC) 12 88 42 42.9%
Local Average Connectivity-based method (LAC) 34 66 37 59.5%
Sum of ECC (SoECC) 37 63 31 54.8%
Range-Limited Centrality (RL) 17 83 42 54.8%
L-index (LI) 13 87 55 58.2%
Leader Rank(LR) 16 84 46 52.2%
Normalized α-Centrality (NC) 11 89 59 64.4%
Moduland-Centrality(MC) 11 89 57 66.7%
  1. The relationships between PeC and fifteen other centrality measures (DC, BC, CC, SC, EC, IC, BN, DMNC, LAC, SoECC, RL, LI, LR, NC, and MC) are studied by evaluating the overlaps between their predicted proteins. For each centrality measure, the top 100 proteins are selected. Then, the number of proteins both predicted by PeC and by anyone of the other centrality measures are calculated.