Method for prediction of protein–protein interactions in yeast using genomics/proteomics information and feature selection

• Published in: Neurocomputing (Amsterdam) 2011-09, Vol.74 (16), p.2683-2690
• Main Authors: Urquiza, J.M., Rojas, I., Pomares, H., Herrera, L.J., Ortega, J., Prieto, A.
• Format: Article
• Language: English
• Subjects: Feature selection; Genomic/proteomic information; Protein–protein interaction; Support vector machines
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2011.03.025

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. From this dataset we extracted 26 proteomics/genomics features using well-known databases and datasets. 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.