Evaluation of machine learning algorithms on protein-protein interactions

Abstract

Protein-protein interactions are important for the majority of biological processes. A significant number of computational methods have been developed to predict protein-protein interactions using proteins' sequence, structural and genomic data. Hence, this fact motivated us to perform a comparative study of various machine learning methods, training them on the set of known protein-protein interactions, using proteins' global and local attributes. The results of the classifiers were evaluated through cross-validation and several performance measures were computed. It was noticed from the results that support vector machine outperformed other classifiers. This fact has also been established through statistical test, called Wilcoxon rank sum test, at 5% significance level.

Published in

Advances in Intelligent Systems and Computing
2014, Volume: 242, number: 242, pages: 211-218 Title: Man-Machine Interactions 3. Advances in Intelligent Systems and Computing
ISBN: 978-3-319-02308-3Publisher: Springer International Publishing Switzerland 2014

SLU Authors

• Klingström, Tomas

  • Department of Animal Biosciences, Swedish University of Agricultural Sciences
    

• Forsberg, Simon

  • Department of Clinical Sciences, Swedish University of Agricultural Sciences
    

• Kierczak, Marcin

  • Department of Clinical Sciences, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Bioinformatics and Systems Biology
Genetics and Breeding
Bioinformatics (Computational Biology)


Publication identifier

DOI: https://doi.org/10.1007/978-3-319-02309-0_22

Permanent link to this page (URI)

https://res.slu.se/id/publ/51961