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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.
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Algorithms for Molecular Biology

ISSN: 1748-7188