Method for Prediction of Protein-Protein Interactions in Yeast Using Genomics/Proteomics Information and Feature Selection

Authors:
J. M. Urquiza;I. Rojas;H. Pomares;J. P. Florido;G. Rubio;L. J. Herrera;J. C. Calvo;J. Ortega
Affiliations:
Department of Computer Architecture and Computer Technology, University of Granada, Granada, Spain 18017;Department of Computer Architecture and Computer Technology, University of Granada, Granada, Spain 18017;Department of Computer Architecture and Computer Technology, University of Granada, Granada, Spain 18017;Department of Computer Architecture and Computer Technology, University of Granada, Granada, Spain 18017;Department of Computer Architecture and Computer Technology, University of Granada, Granada, Spain 18017;Department of Computer Architecture and Computer Technology, University of Granada, Granada, Spain 18017;Department of Computer Architecture and Computer Technology, University of Granada, Granada, Spain 18017;Department of Computer Architecture and Computer Technology, University of Granada, Granada, Spain 18017
Venue:
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Year:
2009

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Abstract

Protein-protein interaction (PPI) prediction is one of the main goals in the current Proteomics. This work presents a method for prediction of protein-protein interactions through a classification technique known as Support Vector Machines. The dataset considered is a set of positive and negative examples taken from a high reliability source, from which we extracted a set of genomic features, proposing a similarity measure. Feature selection was performed to obtain the most relevant variables through a modified method derived from other feature selection methods for classification. Using the selected subset of features, we constructed a support vector classifier that obtains values of specificity and sensitivity higher than 90% in prediction of PPIs, and also providing a confidence score in interaction prediction of each pair of proteins.