Zehua Zhang, Jijun Tang* and Fei Guo* Pages 119 - 127 ( 9 )
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.
Co-expression information, complex detection, gene expression, genes, markov cluster algorithm, PPI network.
School of Computer Science and Technology, Tianjin University, Tianjin, School of Computer Science and Technology, Tianjin University, Tianjin, School of Computer Science and Technology, Tianjin University, Tianjin