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Table 1 Performance comparison between network-based ranking algorithms on different functional similarity protein complex networks

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

#

Protein complex network

NBH

RWR

PRINCE

1

SharedProteinComNet

0.99665 (±0.00988)

0.99134 (±0.01663)

0.99174 (±0.01633)

(P-value = 4.37 × 10−14)

(P-value = 1.7 × 10−12)

2

SharedGOTermComNet

0.95869 (±0.07599)

0.90671 (±0.10804)

0.94305 (±0.08432)

(P-value = 1.28 × 10−27)

(P-value = 8.2 × 10−4)

3

SharedPPIComNet

0.97704 (±0.05926)

0.92646 (±0.12794)

0.96257 (±0.08166)

(P-value = 1.28 × 10−27)

(P-value = 1.51 × 10−4)

  1. –P-values represent the statistical significance of performance comparison between NBH and RWR/PRINCE algorithms. 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 NBH is statistically significantly better than that of RWR and PRINCE for all three functional similarity protein complex networks. Data row #1, #2 and #3 are detail performance comparison of the three algorithms on SharedProteinComNet, SharedGOTermComNet and SharedPPIComNet networks corresponding to Figure 2(a), (b) and (c), respectively.
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Algorithms for Molecular Biology

ISSN: 1748-7188