PCD-GED: Protein complex detection considering PPI dynamics based on time series gene expression data.
Detection of protein complexes from protein-protein interaction (PPI) networks is essential to understand the function of cell machinery. However, available PPIs are static, and cannot reflect the dynamics inherent in real networks. Our method uses time series gene expression data in addition to PPI networks to detect protein complexes. The proposed method generates a series of time-sequenced subnetworks (TSN) according to the time that the interactions are activated. It finds, from each TSN, the protein complexes by employing the weighted clustering coefficient and maximal weighted density concepts. The final set of detected protein complexes are obtained from union of all complexes from different subnetworks. Our findings suggest that by employing these considerations can produce far better results in protein complex detection problem.
Computer Engineering Department, Tarbiat Modares University, Tehran, Iran.
Amir
A
Lakizadeh
Computer Engineering Department, Tarbiat Modares University, Tehran, Iran. Electronic address: sjalili@modares.ac.ir.
Saeed
S
Jalili
Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran.
Sayed-Amir
SA
Marashi
D000465:Algorithms; D000818:Animals; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D015870:Gene Expression; D011485:Protein Binding / Q000502:physiology*; D025941:Protein Interaction Mapping / Q000379:methods*; D060066:Protein Interaction Maps
Clustering coefficient; Protein complex; Protein–protein interaction; Weighted density