Selecting Negative Samples for PPI Prediction Using Hierarchical Clustering Methodology

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 hi...

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Bibliographic Details
Published in:Journal of applied mathematics 2012-01, Vol.2012 (2012), p.1-23
Main Authors: Florido, J. P., Herrera, Luis Javier, Pomares, H., Rojas, I., Urquiza, J. M., Valenzuela, O.
Format: Article
Language:English
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Summary: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 selectedsuboptimum set of features, the constructed support vector machine model is able to classify PPIs with high accuracy in any positive and negative datasets.
ISSN:1110-757X
1687-0042
DOI:10.1155/2012/897289