Selecting Negative Samples for PPI Prediction Using Hierarchical Clustering Methodology

J. M. Urquiza, I. Rojas, H. Pomares, J. Herrera, J. P. Florido and O. Valenzuela

Journal of Applied Mathematics, 2012, vol. 2012, issue 1

Abstract: 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.

Date: 2012
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https://doi.org/10.1155/2012/897289

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Persistent link: https://EconPapers.repec.org/RePEc:wly:jnljam:v:2012:y:2012:i:1:n:897289

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