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2012 Selecting Negative Samples for PPI Prediction Using Hierarchical Clustering Methodology
J. M. Urquiza, I. Rojas, H. Pomares, J. Herrera, J. P. Florido, O. Valenzuela
J. Appl. Math. 2012(SI04): 1-23 (2012). DOI: 10.1155/2012/897289


Protein-protein interactions (PPIs) play a crucial role in cellular processes. In the present work, a new approach is proposed to construct a PPI predictor training a support vector machine model through a mutual information filter-wrapper parallel feature selection algorithm and an iterative and hierarchical clustering to select a relevance negative training set. By means of a selected suboptimum set of features, the constructed support vector machine model is able to classify PPIs with high accuracy in any positive and negative datasets.


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J. M. Urquiza. I. Rojas. H. Pomares. J. Herrera. J. P. Florido. O. Valenzuela. "Selecting Negative Samples for PPI Prediction Using Hierarchical Clustering Methodology." J. Appl. Math. 2012 (SI04) 1 - 23, 2012.


Published: 2012
First available in Project Euclid: 17 October 2012

MathSciNet: MR2889124
Digital Object Identifier: 10.1155/2012/897289

Rights: Copyright © 2012 Hindawi


Vol.2012 • No. SI04 • 2012
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