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Computational Methods for Prediction of Protein-Protein Interactions: PPI Prediction Methods

Computational Methods for Prediction of Protein-Protein Interactions: PPI Prediction Methods

Sneha Rai, Sonika Bhatnagar
Copyright: © 2017 |Pages: 32
ISBN13: 9781522517986|ISBN10: 1522517987|EISBN13: 9781522517993
DOI: 10.4018/978-1-5225-1798-6.ch012
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MLA

Rai, Sneha, and Sonika Bhatnagar. "Computational Methods for Prediction of Protein-Protein Interactions: PPI Prediction Methods." Materials Science and Engineering: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2017, pp. 309-340. https://doi.org/10.4018/978-1-5225-1798-6.ch012

APA

Rai, S. & Bhatnagar, S. (2017). Computational Methods for Prediction of Protein-Protein Interactions: PPI Prediction Methods. In I. Management Association (Ed.), Materials Science and Engineering: Concepts, Methodologies, Tools, and Applications (pp. 309-340). IGI Global. https://doi.org/10.4018/978-1-5225-1798-6.ch012

Chicago

Rai, Sneha, and Sonika Bhatnagar. "Computational Methods for Prediction of Protein-Protein Interactions: PPI Prediction Methods." In Materials Science and Engineering: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 309-340. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-1798-6.ch012

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Abstract

The key signaling pathways in cellular processes involve protein-protein interactions (PPIs). A perturbation in the balance of PPIs occurs in various pathophysiological processes. There are a large numbers of experimental methods for detection of PPIs. However, experimental PPI determination is time consuming, expensive, error prone and does not effectively cover transient interactions. Therefore, overlaying and integration of predictive methods with experimental results provides statistical robustness and biological significance to the PPI data. In this chapter, the authors describe PPIs in terms of types, importance, and biological consequences. This chapter also provides a comprehensive description on various computational approaches for PPI prediction. Prediction of PPI can be done through: 1) Genomic information based methods 2) Structure based methods 3) Network topology based methods: 4) Literature and data mining based methods 5) Machine learning methods. For ease of use and convenience, a summary of various databases and software for PPI prediction has been provided.

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