Table 3.
Overlap and different proteins identified by ION and eight other existing centrality methods
| Centrality measures (Mi) | |ION∩Mi| | |Mi − ION| | nonessential proteins in {Mi − ION} | non-essential proteins percentage in {Mi − ION} with low ION value |
|---|---|---|---|---|
| Degree Centrality (DC) |
1 |
99 |
54 |
59.26% |
| Betweenness Centrality (BC) |
1 |
99 |
56 |
57.14% |
| Closeness Centrality (CC) |
0 |
100 |
59 |
55.93% |
| Subgraph Centrality(SC) |
1 |
99 |
63 |
52.38% |
| Eigenvector Centrality(EC) |
1 |
99 |
63 |
52.38% |
| Information Centrality(IC) |
0 |
100 |
56 |
55.36% |
| Edge Clustering Coefficient Centrality (NC) |
14 |
86 |
42 |
73.81% |
| PCC and ECC centrality(PeC) | 27 | 73 | 24 | 45.83% |
This table shows the common and the difference between ION and the eight other existing centrality methods (DC, BC, CC, SC, EC, IC, NC and PeC) when predicting top 100 proteins. |ION ∩ Mi | denotes the number of proteins identified by both ION and one of the eight other existing centrality methods Mi. {Mi − ION} represents the set of proteins detected by Mi while ignored by ION. |Mi − ION| is the number of proteins in set {Mi − ION}. The last column describes the percentages of different nonessential proteins with low ION scores (less than 0.55) in top 100 proteins.