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.
An Erratum for this chapter can be found at http://dx.doi.org/10.1007/978-3-319-02309-9_71
An erratum to this chapter can be found at http://dx.doi.org/10.1007/978-3-319-02309-0_71
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Saha, I. et al. (2014). Evaluation of Machine Learning Algorithms on Protein-Protein Interactions. In: Gruca, D., Czachórski, T., Kozielski, S. (eds) Man-Machine Interactions 3. Advances in Intelligent Systems and Computing, vol 242. Springer, Cham. https://doi.org/10.1007/978-3-319-02309-0_22
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DOI: https://doi.org/10.1007/978-3-319-02309-0_22
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