Full Text:   <2790>

Summary:  <1910>

CLC number: TP311; R857.3

On-line Access: 2015-04-03

Received: 2014-08-06

Revision Accepted: 2014-12-16

Crosschecked: 2015-03-09

Cited: 3

Clicked: 6496

Citations:  Bibtex RefMan EndNote GB/T7714


Bi-hai Zhao


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Frontiers of Information Technology & Electronic Engineering  2015 Vol.16 No.4 P.293-300


Overlap maximum matching ratio (OMMR): a new measure to evaluate overlaps of essential modules

Author(s):  Xiao-xia Zhang, Qiang-hua Xiao, Bin Li, Sai Hu, Hui-jun Xiong, Bi-hai Zhao

Affiliation(s):  Department of Mathematics and Computer Science, Changsha University, Changsha 410003, China; more

Corresponding email(s):   zhangxx111@yeah.net, bihaizhao@163.com

Key Words:  Protein-protein interaction network, Essential protein modules, Overlap, Overlap maximum matching ratio

Xiao-xia Zhang, Qiang-hua Xiao, Bin Li, Sai Hu, Hui-jun Xiong, Bi-hai Zhao. Overlap maximum matching ratio (OMMR): a new measure to evaluate overlaps of essential modules[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(4): 293-300.

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journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

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%T Overlap maximum matching ratio (OMMR): a new measure to evaluate overlaps of essential modules
%A Xiao-xia Zhang
%A Qiang-hua Xiao
%A Bin Li
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%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1400282

T1 - Overlap maximum matching ratio (OMMR): a new measure to evaluate overlaps of essential modules
A1 - Xiao-xia Zhang
A1 - Qiang-hua Xiao
A1 - Bin Li
A1 - Sai Hu
A1 - Hui-jun Xiong
A1 - Bi-hai Zhao
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 16
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SP - 293
EP - 300
%@ 2095-9184
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1400282

Protein complexes are the basic units of macro-molecular organizations and help us to understand the cell’s mechanism. The development of the yeast two-hybrid, tandem affinity purification, and mass spectrometry high-throughput proteomic techniques supplies a large amount of protein-protein interaction data, which make it possible to predict overlapping complexes through computational methods. Research shows that overlapping complexes can contribute to identifying essential proteins, which are necessary for the organism to survive and reproduce, and for life’s activities. Scholars pay more attention to the evaluation of protein complexes. However, few of them focus on predicted overlaps. In this paper, an evaluation criterion called overlap maximum matching ratio (OMMR) is proposed to analyze the similarity between the identified overlaps and the benchmark overlap modules. Comparison of essential proteins and gene ontology (GO) analysis are also used to assess the quality of overlaps. We perform a comprehensive comparison of serveral overlapping complexes prediction approaches, using three yeast protein-protein interaction (PPI) networks. We focus on the analysis of overlaps identified by these algorithms. Experimental results indicate the important of overlaps and reveal the relationship between overlaps and identification of essential proteins.

In this paper, authors propose a new measure, namely OMMR, to evaluate the quality of overlaps of protein complexes identified. In general, unlike previous works that only focus on the overlapping between two protein complexes, this new measure targets to compute the overall overlapping score for all protein complexes identified.




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