Journal article
Neighbor affinity based algorithm for discovering temporal protein complex from dynamic PPI network
Methods (San Diego, Calif.), Vol.110
01 Nov 2016
PMID: 27320204
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Detection of temporal protein complexes would be a great aid in furthering our knowledge of the dynamic features and molecular mechanism in cell life activities. Most existing clustering algorithms for discovering protein complexes are based on static protein interaction networks in which the inherent dynamics are often overlooked. We propose a novel algorithm DPC-NADPIN (Discovering Protein Complexes based on Neighbor Affinity and Dynamic Protein Interaction Network) to identify temporal protein complexes from the time course protein interaction networks. Inspired by the idea of that the tighter a protein's neighbors inside a module connect, the greater the possibility that the protein belongs to the module, DPC-NADPIN algorithm first chooses each of the proteins with high clustering coefficient and its neighbors to consolidate into an initial cluster, and then the initial cluster becomes a protein complex by appending its neighbor proteins according to the relationship between the affinity among neighbors inside the cluster and that outside the cluster. In our experiments, DPC-NADPIN algorithm is proved to be reasonable and it has better performance on discovering protein complexes than the following state-of-the-art algorithms: Hunter, MCODE, CFinder, SPICI, and ClusterONE; Meanwhile, it obtains many protein complexes with strong biological significance, which provide helpful biological knowledge to the related researchers. Moreover, we find that proteins are assembled coordinately to form protein complexes with characteristics of temporality and spatiality, thereby performing specific biological functions.
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Details
- Title
- Neighbor affinity based algorithm for discovering temporal protein complex from dynamic PPI network
- Creators
- Xianjun Shen - Central China Normal UniversityLi Yi - Central China Normal UniversityXingpeng Jiang - Central China Normal UniversityYanli Zhao - Central China Normal UniversityXiaohua Hu - Central China Normal UniversityTingting He - Central China Normal UniversityJincai Yang - Central China Normal University
- Publication Details
- Methods (San Diego, Calif.), Vol.110
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science (Informatics)
- Identifiers
- 991019167335904721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Biochemical Research Methods
- Biochemistry & Molecular Biology