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Evaluation of Machine Learning Algorithms on Protein-Protein Interactions

  • Indrajit Saha
  • Tomas Klingström
  • Simon Forsberg
  • Johan Wikander
  • Julian Zubek
  • Marcin Kierczak
  • Dariusz Plewczynski
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 242)

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.

Keywords

bioinformatics machine learning protein-protein interactions 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Indrajit Saha
    • 1
  • Tomas Klingström
    • 2
  • Simon Forsberg
    • 3
  • Johan Wikander
    • 4
  • Julian Zubek
    • 5
  • Marcin Kierczak
    • 3
  • Dariusz Plewczynski
    • 2
  1. 1.Department of Computer Science and EngineeringJadavpur UniversityKolkataIndia
  2. 2.Interdisciplinary Centre for Mathematical and Computational ModelingUniversity of WarsawWarsawPoland
  3. 3.Department of Clinical Sciences, Computational Genetics SectionSwedish University of Agricultural SciencesUppsalaSweden
  4. 4.Bioinformatics Program, Faculty of Technology and Natural SciencesUppsala UniversityUppsalaSweden
  5. 5.Institute of Computer SciencePolish Academy of SciencesWarsawPoland

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