Complex Detection in PPI Network Using Genes Expression Information

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.