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Chapter 4: Protein Interactions and Disease

Overview of attention for article published in PLoS Computational Biology, December 2012
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Title
Chapter 4: Protein Interactions and Disease
Published in
PLoS Computational Biology, December 2012
DOI 10.1371/journal.pcbi.1002819
Pubmed ID
Authors

Mileidy W. Gonzalez, Maricel G. Kann

Abstract

Proteins do not function in isolation; it is their interactions with one another and also with other molecules (e.g. DNA, RNA) that mediate metabolic and signaling pathways, cellular processes, and organismal systems. Due to their central role in biological function, protein interactions also control the mechanisms leading to healthy and diseased states in organisms. Diseases are often caused by mutations affecting the binding interface or leading to biochemically dysfunctional allosteric changes in proteins. Therefore, protein interaction networks can elucidate the molecular basis of disease, which in turn can inform methods for prevention, diagnosis, and treatment. In this chapter, we will describe the computational approaches to predict and map networks of protein interactions and briefly review the experimental methods to detect protein interactions. We will describe the application of protein interaction networks as a translational approach to the study of human disease and evaluate the challenges faced by these approaches.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 534 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 6 1%
United States 5 <1%
Spain 4 <1%
Brazil 4 <1%
Italy 3 <1%
Germany 2 <1%
France 2 <1%
Japan 2 <1%
Canada 1 <1%
Other 2 <1%
Unknown 503 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 117 22%
Researcher 80 15%
Student > Master 69 13%
Student > Bachelor 52 10%
Student > Postgraduate 25 5%
Other 77 14%
Unknown 114 21%
Readers by discipline Count As %
Agricultural and Biological Sciences 128 24%
Biochemistry, Genetics and Molecular Biology 110 21%
Chemistry 45 8%
Computer Science 38 7%
Medicine and Dentistry 29 5%
Other 59 11%
Unknown 125 23%