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Comparative analysis of differential network modularity in tissue specific normal and cancer protein interaction networks

Overview of attention for article published in Journal of Clinical Bioinformatics, October 2013
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Citations

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33 Mendeley
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Title
Comparative analysis of differential network modularity in tissue specific normal and cancer protein interaction networks
Published in
Journal of Clinical Bioinformatics, October 2013
DOI 10.1186/2043-9113-3-19
Pubmed ID
Authors

Md Fahmid Islam, Md Moinul Hoque, Rajat Suvra Banik, Sanjoy Roy, Sharmin Sultana Sumi, F M Nazmul Hassan, Md Tauhid Siddiki Tomal, Ahmad Ullah, K M Taufiqur Rahman

Abstract

Large scale understanding of complex and dynamic alterations in cellular and subcellular levels during cancer in contrast to normal condition has facilitated the emergence of sophisticated systemic approaches like network biology in recent times. As most biological networks show modular properties, the analysis of differential modularity between normal and cancer protein interaction networks can be a good way to understand cancer more significantly. Two aspects of biological network modularity e.g. detection of molecular complexes (potential modules or clusters) and identification of crucial nodes forming the overlapping modules have been considered in this regard.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 3%
Korea, Republic of 1 3%
Saudi Arabia 1 3%
Unknown 30 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 18%
Student > Ph. D. Student 5 15%
Student > Master 5 15%
Student > Bachelor 3 9%
Professor > Associate Professor 2 6%
Other 6 18%
Unknown 6 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 30%
Biochemistry, Genetics and Molecular Biology 5 15%
Computer Science 5 15%
Social Sciences 2 6%
Unspecified 1 3%
Other 3 9%
Unknown 7 21%