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Protein – Protein Interaction

Overview of attention for book
Attention for Chapter 84: Using product kernels to predict protein interactions.
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Chapter title
Using product kernels to predict protein interactions.
Chapter number 84
Book title
Protein – Protein Interaction
Published in
Advances in biochemical engineering biotechnology, January 2008
DOI 10.1007/10_2007_084
Pubmed ID
Book ISBNs
978-3-54-068817-4, 978-3-54-068820-4
Authors

Martin, Shawn, Brown, W Michael, Faulon, Jean-Loup, Brown, W. Michael, Shawn Martin, W. Michael Brown, Jean-Loup Faulon

Abstract

There is a wide variety of experimental methods for the identification of protein interactions. This variety has in turn spurred the development of numerous different computational approaches for modeling and predicting protein interactions. These methods range from detailed structure-based methods capable of operating on only a single pair of proteins at a time to approximate statistical methods capable of making predictions on multiple proteomes simultaneously. In this chapter, we provide a brief discussion of the relative merits of different experimental and computational methods available for identifying protein interactions. Then we focus on the application of our particular (computational) method using Support Vector Machine product kernels. We describe our method in detail and discuss the application of the method for predicting protein-protein interactions, beta-strand interactions, and protein-chemical interactions.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 4%
Uruguay 1 4%
France 1 4%
Germany 1 4%
Unknown 24 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 29%
Student > Ph. D. Student 7 25%
Professor 3 11%
Student > Master 3 11%
Professor > Associate Professor 2 7%
Other 3 11%
Unknown 2 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 54%
Biochemistry, Genetics and Molecular Biology 3 11%
Engineering 2 7%
Computer Science 2 7%
Chemistry 2 7%
Other 2 7%
Unknown 2 7%