Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Cao, Buwena; c; * | Deng, Shuguangb | Luo, Jiaweic | Ding, Pingjianc | Wang, Shulinc
Affiliations: [a] School of Information Science and Engineering, Hunan City University, Yiyang, China | [b] College of Communication and Electronic Engineering, Hunan City University, Yiyang, China | [c] College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
Correspondence: [*] Corresponding author. Buwen Cao, School of Information Science and Engineering, Hunan City University, Yiyang 413000, China. E-mail: [email protected].
Abstract: The identification of overlapping protein complexes in proteinprotein interaction (PPI) networks may elucidate cellular functional organizations and their underlying cellular mechanisms. Recently, many protein complex mining algorithms have been developed for PPI networks. However, the majority of available algorithms primarily depend on mining dense subgraphs as protein complexes, thereby failing to consider the inherent biological meanings between protein pairs. Thus, methods for identifying protein complexes using the biological significance hidden in edges need to be investigated. In this paper, we propose IK-medoids, an improved method that detects overlapping protein complexes from weighted PPI networks based on the rough fuzzy relationships between protein pairs. The presented algorithm is primarily based on the fuzzy relationship that obtains the non-overlapping protein substructure, and then K-medoids is executed from the proteins in the PPI network. Next, the similarity between one protein and each candidate complex is calculated to determine whether the protein belongs to one or multiple complexes with the ration of each similarity to maximum similarity. In the end, overlapped protein complexes are merged to form the final protein complexes. We apply the method to three PPI networks and validate the results using two reference protein complexes retrieved from public databases. Experimental results show that our method outperforms classical algorithms, such as ClusterONE, CMC, MCL, OSLOM, and RFC, and achieves ideal overall performance in terms of F-measure, sensitivity, and accuracy.
Keywords: PPI, protein complex, overlapping, K-medoids, Fuzzy relation
DOI: 10.3233/JIFS-17026
Journal: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 1, pp. 93-103, 2018
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
[email protected]
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office [email protected]
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
如果您在出版方面需要帮助或有任何建, 件至: [email protected]