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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yang Guo Xuequn Shang Qingping Zhu Mingkui Huang Zhanhuai Li |
| Copyright Year | 2014 |
| Description | Author affiliation: Dept. of Comput. Sci. & Technol., Northwestern Polytech. Univ., Xi'an, China (Yang Guo; Xuequn Shang; Qingping Zhu; Mingkui Huang; Zhanhuai Li) |
| Abstract | Mining the protein complexes and functional modules from protein-protein interaction (PPI) networks is vital to understand the mechanism of cellular components and protein functions. Most of the proposed methods had solely focused on static properties of the PPI networks since the available PPI data are static. However, cellular systems are highly dynamic. That is, the interactions of proteins are responsive to environmental cues to accomplish diverse cellular functions. It is important to consider the dynamic inherent within the PPI networks to identify protein complexes and functional modules. In addition, most computational methods did not distinguish between protein complexes and functional modules. It is important to distinguish between them since they are different protein organizations. In this paper, we propose a novel framework to analyze the PPI networks in dynamic conditions by integrating time-series gene expression profiles data and subcelluar localization data. The algorithm, CBMI, is developed to identify protein complexes in integrated PPI networks. By investigating multiple perspectives of proteins in the PPI networks, we identify the “dynamic” hubs in the PPI networks, and then present a new method to discover the functional modules in the PPI networks. The experimental results show that the integration of temporal gene expression data and subcelluar localization data with PPI data contributes to extracting the protein complexes more precisely. Comprehensive evaluations based on f-measure and functional annotations in MIPS database reveal that our algorithm, CBMI, outperforms other previous algorithms in identifying protein complexes, and the detected functional modules are statistically significant in terms of functional annotations. The proposed framework provides a new clue to distinguish between protein complexes and functional modules, and the developed algorithms can be an effective technique for the identification of them. |
| Starting Page | 8 |
| Ending Page | 15 |
| File Size | 400678 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781479956692 |
| DOI | 10.1109/BIBM.2014.6999291 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-11-02 |
| Publisher Place | UK |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Proteins functional module Protein engineering Sensitivity PPI network Heuristic algorithms multi-information Clustering algorithms Gene expression protein complex |
| Content Type | Text |
| Resource Type | Article |
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