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.
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© 2009 Springer-Verlag Berlin Heidelberg
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Urquiza, J.M. et al. (2009). Method for Prediction of Protein-Protein Interactions in Yeast Using Genomics/Proteomics Information and Feature Selection. In: Cabestany, J., Sandoval, F., Prieto, A., Corchado, J.M. (eds) Bio-Inspired Systems: Computational and Ambient Intelligence. IWANN 2009. Lecture Notes in Computer Science, vol 5517. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02478-8_107
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DOI: https://doi.org/10.1007/978-3-642-02478-8_107
Publisher Name: Springer, Berlin, Heidelberg
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