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Machine learning multi-classifiers for peptide classification

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Abstract

In this paper, we study the performance improvement that it is possible to obtain combining classifiers based on different notions (each trained using a different physicochemical property of amino-acids). This multi-classifier has been tested in three problems: HIV-protease; recognition of T-cell epitopes; predictive vaccinology. We propose a multi-classifier that combines a classifier that approaches the problem as a two-class pattern recognition problem and a method based on a one-class classifier. Several classifiers combined with the “sum rule” enables us to obtain an improvement performance over the best results previously published in the literature.

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Notes

  1. With Gamma (parameter of the radial based kernel) = 0.25 and C (Cost of the constrain violation) = 1.5.

  2. Implemented as PRTools 3.17 Matlab Toolbox.

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Acknowledgments

The author would like to thank Ivana Bozic, T. Rögnvaldsson and Y. Zhao for sharing the datasets.

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Authors and Affiliations

  1. DEIS, IEIIT – CNR, Università di Bologna, Viale Risorgimento 2, 40136, Bologna, Italy

    Loris Nanni & Alessandra Lumini

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  1. Loris Nanni
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  2. Alessandra Lumini
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Correspondence to Loris Nanni.

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Nanni, L., Lumini, A. Machine learning multi-classifiers for peptide classification. Neural Comput & Applic 18, 185–192 (2009). https://doi.org/10.1007/s00521-007-0170-2

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