Background: Identifying of protein complexes from PPI networks has become a key problem
to elucidate protein functions and identify signaling and biological processes in a cell.
Objective: Accurate determination of complexes in PPI networks is crucial for understanding principles
of cellular organization.
Method: We propose a novel method to identify protein complexes on PPI networks. First, we use
Markov Cluster Algorithm with an edge-weighting scheme to calculate complexes on PPI networks.
Second, we design a new co-expression analysis method to measure each protein complex, based on
differential co-expression information.
Results: To evaluate our method, we experiment on two yeast PPI networks. On DIP network, our
method has Precision and F-Measure values of 0.5014 and 0.5219, which improves upon Precision and
F-Measure values of 0.2896 and 0.3211 for COACH, 0.4252 and 0.3675 for ClusterONE. On MIPS
network, our method has F-Measure values of 0.3597, which improves upon F-Measure values of
0.2497 for COACH, 0.3326 for ClusterONE.
Conclusion: Our method achieves better results than some state-of-the-art methods for identifying protein
complexes on dynamic PPI networks, with the prediction improved.