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Table 2 Performance comparison between functional similarity protein complex networks on different network-based ranking algorithms

From: A novel method for identifying disease associated protein complexes based on functional similarity protein complex networks

# Algorithm SharedProteinComNet SharedGOTermComNet SharedPPIComNet
1 NBH 0.99665 (±0.00988) 0.95869 (±0.07599) 0.97704 (±0.05926)
(P-value = 6.74 × 10−15) (P-value = 8.43 × 10−8)
2 RWR 0.99134 (±0.01663) 0.90671 (±0.10804) 0.92646 (±0.12794)
(P-value = 1.06 × 10−252) (P-value = 7.61 × 10−122)
3 PRINCE 0.99174 (±0.01633) 0.94305 (±0.08432) 0.96257 (±0.08166)
(P-value = 7.14 × 10−151) (P-value = 1.14 × 10−63)
  1. –P-values represent the statistical significance of performance comparison between SharedProteinComNet and SharedGOTermComNet/SharedPPIComNet networks. They were calculated using two-sample t-Test for mean assuming unequal variances.
  2. –Data in each cell represent mean (±standard deviation).
  3. Performance of SharedProteinComNet is statistically significantly better than that of SharedGOTermComNet and SharedPPIComNet for all three network-based ranking algorithms. Data row #1, #2 and #3 are detail performance comparison of the three functional similarity protein complex networks by NBH, RWR and PRINCE algorithms corresponding to Figure 3(a), (b) and (c), respectively.