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Parkinson’s Disease: Bioinspired Optimization Algorithms for Omics Datasets Monitoring

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Handbook of Computational Neurodegeneration

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Abstract

Omics data create several computational challenges related to their volume, high dimensionality, and complexity. A subfield of the computational intelligence area that can address part of this complexity is bioinspired algorithms. These methods are gaining importance by strategies upon modeling the behavior of living organisms to create an algorithmic process that solves optimization problems. There is a remarkable progress in the application of bioinspired algorithms in neurodegeneration diseases from omics data, such as in Parkinson’s disease, a progressive disorder that affects the nervous system and the parts of the body controlled by the nerves. In this chapter, we address the main cutting-edge bioinspired optimization methods applied to PD omics datasets driven by high-throughput analytical workflows offering a significant clinical contribution and providing a more comprehensive outcome across multiple biological layers.

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Acknowledgments

The research work was supported by the Hellenic Foundation for Research and Innovation (HFRI) under the 3rd Call for HFRI PhD Fellowships (Fellowship Number: 6620).

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Authors and Affiliations

  1. Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece

    Konstantina Skolariki, Marios G. Krokidis, Aristidis G. Vrahatis, Themis P. Exarchos & Panagiotis Vlamos

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  1. Konstantina Skolariki
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  2. Marios G. Krokidis
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  3. Aristidis G. Vrahatis
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  4. Themis P. Exarchos
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  5. Panagiotis Vlamos
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Corresponding author

Correspondence to Konstantina Skolariki .

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Editors and Affiliations

  1. Department of Informatics, Ionian University Department of Informatics, Corfu, Greece

    Panayiotis Vlamos

  2. Dept of Physics and Computer Science, Wilfrid Laurier University, Waterloo, ON, Canada

    Ilias S. Kotsireas

  3. ResearchGate, ETH Zurich ResearchGate, Zurich, Zürich, Switzerland

    Ioannis Tarnanas

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Skolariki, K., Krokidis, M.G., Vrahatis, A.G., Exarchos, T.P., Vlamos, P. (2023). Parkinson’s Disease: Bioinspired Optimization Algorithms for Omics Datasets Monitoring. In: Vlamos, P., Kotsireas, I.S., Tarnanas, I. (eds) Handbook of Computational Neurodegeneration. Springer, Cham. https://doi.org/10.1007/978-3-319-75479-6_46-1

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  • DOI: https://doi.org/10.1007/978-3-319-75479-6_46-1

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  • Print ISBN: 978-3-319-75479-6

  • Online ISBN: 978-3-319-75479-6

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