Title: A framework for identifying functional modules in dynamic networks

Authors: Xiwei Tang; Xueyong Li; Sai Hu; Bihai Zhao

Addresses: School of Computer Engineering and Applied Mathematics, Changsha University, Changsha 410022, China; School of Information Science and Engineering, Hunan First Normal University, Changsha 410205, China ' School of Computer Engineering and Applied Mathematics, Changsha University, Changsha 410022, China ' School of Computer Engineering and Applied Mathematics, Changsha University, Changsha 410022, China ' School of Computer Engineering and Applied Mathematics, Changsha University, Changsha 410022, China

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.

Keywords: functional module; dynamic network; core-attachment; protein-protein interaction; GO annotation.

DOI: 10.1504/IJDMB.2018.095554

International Journal of Data Mining and Bioinformatics, 2018 Vol.21 No.1, pp.1 - 17

Received: 04 Sep 2018
Accepted: 04 Sep 2018
Published online: 09 Oct 2018 *

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