A framework for identifying functional modules in dynamic networks

Abstract

Detecting functional modules in Protein-Protein Interaction (PPI) networks is essential to understand gene function, biological pathways and cellular organisation. Majority of methods predict functional modules via the static PPI networks. However, cellular systems are highly dynamic and regulated by the biological networks. Considering the dynamic inherent within these networks, we build the time course PPI networks in terms of the gene expression profiles. And then a novel framework for identifying functional modules with core-attachment structure has been proposed in accordance with the dynamic PPI networks. Our algorithm generates the cores by mining co-expression neighbourhood graphs with an aggregation degree over a threshold and expands them to form functional modules. The method is compared with other competing algorithms based on two different yeast PPI networks. The results show that the proposed framework outperforms state-of-the-art methods.