Selecting Negative Samples for PPI Prediction Using Hierarchical Clustering Methodology

Abstract EN

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.