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DOI: 10.1371/journal.pone.0091856
论文题名:
Detecting Overlapping Protein Complexes by Rough-Fuzzy Clustering in Protein-Protein Interaction Networks
作者: Hao Wu; Lin Gao; Jihua Dong; Xiaofei Yang
刊名: PLOS ONE
ISSN: 1932-6203
出版年: 2014
发表日期: 2014-3-18
卷: 9, 期:3
语种: 英语
英文关键词: Social networks ; Protein complexes ; Algorithms ; Protein interaction networks ; Clustering algorithms ; Dolphins ; Genetic networks ; Protein-protein interactions
英文摘要: In this paper, we present a novel rough-fuzzy clustering (RFC) method to detect overlapping protein complexes in protein-protein interaction (PPI) networks. RFC focuses on fuzzy relation model rather than graph model by integrating fuzzy sets and rough sets, employs the upper and lower approximations of rough sets to deal with overlapping complexes, and calculates the number of complexes automatically. Fuzzy relation between proteins is established and then transformed into fuzzy equivalence relation. Non-overlapping complexes correspond to equivalence classes satisfying certain equivalence relation. To obtain overlapping complexes, we calculate the similarity between one protein and each complex, and then determine whether the protein belongs to one or multiple complexes by computing the ratio of each similarity to maximum similarity. To validate RFC quantitatively, we test it in Gavin, Collins, Krogan and BioGRID datasets. Experiment results show that there is a good correspondence to reference complexes in MIPS and SGD databases. Then we compare RFC with several previous methods, including ClusterONE, CMC, MCL, GCE, OSLOM and CFinder. Results show the precision, sensitivity and separation are 32.4%, 42.9% and 81.9% higher than mean of the five methods in four weighted networks, and are 0.5%, 11.2% and 66.1% higher than mean of the six methods in five unweighted networks. Our method RFC works well for protein complexes detection and provides a new insight of network division, and it can also be applied to identify overlapping community structure in social networks and LFR benchmark networks.
URL: http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0091856&type=printable
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/18845
Appears in Collections:过去全球变化的重建
影响、适应和脆弱性
科学计划与规划
气候变化与战略
全球变化的国际研究计划
气候减缓与适应
气候变化事实与影响

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作者单位: School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi, China;School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi, China;Foreign Language Department, Northwest A&F University, Yangling, Shaanxi, China;School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi, China

Recommended Citation:
Hao Wu,Lin Gao,Jihua Dong,et al. Detecting Overlapping Protein Complexes by Rough-Fuzzy Clustering in Protein-Protein Interaction Networks[J]. PLOS ONE,2014-01-01,9(3)
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