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Prediction of Bacterial Immunogenicity by Machine Learning Methods

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Computational Vaccine Design

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2673))

Abstract

Prediction of bacterial immunogens is a prerequisite for the process of vaccine development through reverse vaccinology. The application of in silico methods allows significant reduction in time and cost for the discovery of potential vaccine candidates among proteins of a bacterial species. The steps in the prediction algorithm include collection of protein sequence datasets of known bacterial immunogens and non-immunogens, data preprocessing to transform the protein sequences into numerical matrices suitable for use as training and test sets for various machine learning methods, and derivation of predictive models. The performance of the derived models is evaluated by means of classification metrics.

In this chapter, we present a protocol for predicting bacterial immunogenicity by applying machine learning methods. The protocol describes the process of model development from data collection and manipulation to training and validation of the derived models.

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Acknowledgment

This work was supported by the Science and Education for Smart Growth Operational Program and co-financed by the European Union through the European Structural and Investment funds (Grant No BG05M2OP001-1.001-0003).

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Authors and Affiliations

  1. Faculty of Pharmacy, Medical University – Sofia, Sofia, Bulgaria

    Ivan Dimitrov & Irini Doytchinova

Authors
  1. Ivan Dimitrov
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  2. Irini Doytchinova
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Corresponding author

Correspondence to Ivan Dimitrov .

Editor information

Editors and Affiliations

  1. School of Medicine, Department of Immunology, Complutense University of Madrid, Madrid, Spain

    Pedro A. Reche

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© 2023 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

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Dimitrov, I., Doytchinova, I. (2023). Prediction of Bacterial Immunogenicity by Machine Learning Methods. In: Reche, P.A. (eds) Computational Vaccine Design. Methods in Molecular Biology, vol 2673. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3239-0_20

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  • DOI: https://doi.org/10.1007/978-1-0716-3239-0_20

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3238-3

  • Online ISBN: 978-1-0716-3239-0

  • eBook Packages: Springer Protocols

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