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Bayesian Clustering of Multivariate Immunological Data

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Book cover Machine Learning, Optimization, and Data Science (LOD 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11331))

Abstract

Given a dataset of B cell subpopulation quantities, for about six thousand patients, that is a cross-sectional immunological dataset, here we detect clusters representing models of immune system states in an unsupervised way (i.e., according only to their different statistical properties). Two time-evolving B cell networks are also generated from data-driven hidden Markov models, with four and five hidden states, respectively. Our interpretation from a biomedical viewpoint of the statistical parameters of the Bayesian models confirms an age related decline of some types of B cell functions and finds out a class of old patients with unexpected B cell values.

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Acknowledgments

Authors would like to thank Antonio Vella (department of pathology and diagnostics, University Hospital of Verona) for providing the dataset used in this work and for interesting discussions on the role of B cells in the immune system.

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Authors and Affiliations

  1. Department of Computer Science, Verona University, Strada Le Grazie 15, 37134, Verona, Italy

    Alberto Castellini & Giuditta Franco

Authors
  1. Alberto Castellini
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  2. Giuditta Franco
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Corresponding author

Correspondence to Alberto Castellini .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy and University of Reading, Reading, UK

    Giuseppe Nicosia

  2. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  3. University of Catania, Catania, Italy

    Giovanni Giuffrida

  4. Harvard University, Cambridge, MA, USA

    Renato Umeton

  5. IBM, Tivoli Research Lab, Rome, Italy

    Vincenzo Sciacca

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Castellini, A., Franco, G. (2019). Bayesian Clustering of Multivariate Immunological Data. In: Nicosia, G., Pardalos, P., Giuffrida, G., Umeton, R., Sciacca, V. (eds) Machine Learning, Optimization, and Data Science. LOD 2018. Lecture Notes in Computer Science(), vol 11331. Springer, Cham. https://doi.org/10.1007/978-3-030-13709-0_43

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  • DOI: https://doi.org/10.1007/978-3-030-13709-0_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-13708-3

  • Online ISBN: 978-3-030-13709-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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