Selecting Negative Samples for PPI Prediction Using Hierarchical Clustering Methodology. (4th March 2012)

• Record Type: Journal Article
• Authors: Urquiza, J. M.; Rojas, I.; Pomares, H.; Herrera, J.; Florido, J. P.; Valenzuela, O.
• Other Names: Krishnan Venky Academic Editor.
• Is Part Of: Journal of applied mathematics. Volume 2012(2012)
• Publication Date: 2012-03-04
• Subjects: Mathematics -- Periodicals; 519.05
• DOI: 10.1155/2012/897289
• Languages: English
• ISSNs: 1110-757X

Abstract

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.