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Int. J. Mol. Sci. 2016, 17(11), 1946; doi:10.3390/ijms17111946

Prediction of Protein–Protein Interactions by Evidence Combining Methods

1
National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
2
College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
*
Author to whom correspondence should be addressed.
Academic Editors: Tatyana Karabencheva-Christova and Christo Z. Christov
Received: 30 September 2016 / Revised: 15 November 2016 / Accepted: 15 November 2016 / Published: 22 November 2016
(This article belongs to the Collection Proteins and Protein-Ligand Interactions)
View Full-Text   |   Download PDF [3095 KB, uploaded 22 November 2016]   |  

Abstract

Most cellular functions involve proteins’ features based on their physical interactions with other partner proteins. Sketching a map of protein–protein interactions (PPIs) is therefore an important inception step towards understanding the basics of cell functions. Several experimental techniques operating in vivo or in vitro have made significant contributions to screening a large number of protein interaction partners, especially high-throughput experimental methods. However, computational approaches for PPI predication supported by rapid accumulation of data generated from experimental techniques, 3D structure definitions, and genome sequencing have boosted the map sketching of PPIs. In this review, we shed light on in silico PPI prediction methods that integrate evidence from multiple sources, including evolutionary relationship, function annotation, sequence/structure features, network topology and text mining. These methods are developed for integration of multi-dimensional evidence, for designing the strategies to predict novel interactions, and for making the results consistent with the increase of prediction coverage and accuracy. View Full-Text
Keywords: interaction prediction; PPIs; physical interactions; support vector machine interaction prediction; PPIs; physical interactions; support vector machine
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Chang, J.-W.; Zhou, Y.-Q.; Ul Qamar, M.T.; Chen, L.-L.; Ding, Y.-D. Prediction of Protein–Protein Interactions by Evidence Combining Methods. Int. J. Mol. Sci. 2016, 17, 1946.

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