Selecting Negative Samples for PPI Prediction Using Hierarchical Clustering Methodology - J. M. Urquiza; I. Rojas; H. Pomares; J. Herrera; J. P. Florido; O. Valenzuela (Journal of Applied Mathematics)
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.