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Identifying protein complexes based on density and modularity in protein-protein interaction network

Overview of attention for article published in BMC Systems Biology, October 2013
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Title
Identifying protein complexes based on density and modularity in protein-protein interaction network
Published in
BMC Systems Biology, October 2013
DOI 10.1186/1752-0509-7-s4-s12
Pubmed ID
Authors

Jun Ren, Jianxin Wang, Min Li, Lusheng Wang

Abstract

Identifying protein complexes is crucial to understanding principles of cellular organization and functional mechanisms. As many evidences have indicated that the subgraphs with high density or with high modularity in PPI network usually correspond to protein complexes, protein complexes detection methods based on PPI network focused on subgraph's density or its modularity in PPI network. However, dense subgraphs may have low modularity and subgraph with high modularity may have low density, which results that protein complexes may be subgraphs with low modularity or with low density in the PPI network. As the density-based methods are difficult to mine protein complexes with low density, and the modularity-based methods are difficult to mine protein complexes with low modularity, both two methods have limitation for identifying protein complexes with various density and modularity.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 25%
Student > Master 3 25%
Researcher 2 17%
Professor > Associate Professor 1 8%
Unknown 3 25%
Readers by discipline Count As %
Computer Science 4 33%
Biochemistry, Genetics and Molecular Biology 1 8%
Mathematics 1 8%
Agricultural and Biological Sciences 1 8%
Unknown 5 42%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 November 2014.
All research outputs
#16,628,262
of 18,796,975 outputs
Outputs from BMC Systems Biology
#986
of 1,127 outputs
Outputs of similar age
#202,337
of 243,778 outputs
Outputs of similar age from BMC Systems Biology
#56
of 67 outputs
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