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Int. J. Mol. Sci. 2015, 16(3), 4774-4785; doi:10.3390/ijms16034774

Efficient Prediction of Progesterone Receptor Interactome Using a Support Vector Machine Model

College of Veterinary Medicine, South China Agricultural University, Guangzhou 510642, China
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Academic Editor: Christo Z. Christov
Received: 14 January 2015 / Revised: 20 February 2015 / Accepted: 25 February 2015 / Published: 3 March 2015
(This article belongs to the Collection Proteins and Protein-Ligand Interactions)
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Abstract

Protein-protein interaction (PPI) is essential for almost all cellular processes and identification of PPI is a crucial task for biomedical researchers. So far, most computational studies of PPI are intended for pair-wise prediction. Theoretically, predicting protein partners for a single protein is likely a simpler problem. Given enough data for a particular protein, the results can be more accurate than general PPI predictors. In the present study, we assessed the potential of using the support vector machine (SVM) model with selected features centered on a particular protein for PPI prediction. As a proof-of-concept study, we applied this method to identify the interactome of progesterone receptor (PR), a protein which is essential for coordinating female reproduction in mammals by mediating the actions of ovarian progesterone. We achieved an accuracy of 91.9%, sensitivity of 92.8% and specificity of 91.2%. Our method is generally applicable to any other proteins and therefore may be of help in guiding biomedical experiments. View Full-Text
Keywords: protein–protein interaction; support vector machine; progesterone receptor protein–protein interaction; support vector machine; progesterone receptor
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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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Liu, J.-L.; Peng, Y.; Fu, Y.-S. Efficient Prediction of Progesterone Receptor Interactome Using a Support Vector Machine Model. Int. J. Mol. Sci. 2015, 16, 4774-4785.

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