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Multi-objective Evolutionary Discretization of Gene Expression Profiles: Application to COVID-19 Severity Prediction

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Applications of Evolutionary Computation (EvoApplications 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13989))

Abstract

Machine learning models can use information from gene expressions in patients to efficiently predict the severity of symptoms for several diseases. Medical experts, however, still need to understand the reasoning behind the predictions before trusting them. In their day-to-day practice, physicians prefer using gene expression profiles, consisting of a discretized subset of all data from gene expressions: in these profiles, genes are typically reported as either over-expressed or under-expressed, using discretization thresholds computed on data from a healthy control group. A discretized profile allows medical experts to quickly categorize patients at a glance. Building on previous works related to the automatic discretization of patient profiles, we present a novel approach that frames the problem as a multi-objective optimization task: on the one hand, after discretization, the medical expert would prefer to have as few different profiles as possible, to be able to classify patients in an intuitive way; on the other hand, the loss of information has to be minimized. Loss of information can be estimated using the performance of a classifier trained on the discretized gene expression levels. We apply one common state-of-the-art evolutionary multi-objective algorithm, NSGA-II, to the discretization of a dataset of COVID-19 patients that developed either mild or severe symptoms. The results show not only that the solutions found by the approach dominate traditional discretization based on statistical analysis and are more generally valid than those obtained through single-objective optimization, but that the candidate Pareto-optimal solutions preserve the sense-making that practitioners find necessary to trust the results.

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Notes

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    https://pythonhosted.org/inspyred/.

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    https://www.ncbi.nlm.nih.gov/geo/.

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Author information

Authors and Affiliations

  1. Division of Pharmacology, University of Utrecht, Universiteitsweg 99, 3584 CG, Utrecht, The Netherlands

    David Rojas-Velazquez, Aletta D. Kraneveld & Alejandro Lopez-Rincon

  2. Department of Data Science, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands

    David Rojas-Velazquez & Alejandro Lopez-Rincon

  3. UMR 518 MIA-PS, INRAE, Université Paris-Saclay, 91120, Palaiseau, France

    Alberto Tonda

  4. Institut des Systèmes Complexes de Paris Île-de-France (ISC-PIF) - UAR 3611 CNRS, Paris, France

    Alberto Tonda

  5. Centro de Investigaciones en Ciencias Microbiologícas, Instituto de Ciencias, Benemérita Universidad Autónoma de Puebla IC 11 Puebla, Puebla, Mexico

    Itzel Rodriguez-Guerra

Authors
  1. David Rojas-Velazquez
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  2. Alberto Tonda
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  3. Itzel Rodriguez-Guerra
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  4. Aletta D. Kraneveld
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  5. Alejandro Lopez-Rincon
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Corresponding author

Correspondence to David Rojas-Velazquez .

Editor information

Editors and Affiliations

  1. University of Coimbra, Coimbra, Portugal

    João Correia

  2. University of York, York, UK

    Stephen Smith

  3. Al Hussein Technical University, Amman, Jordan

    Raneem Qaddoura

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Rojas-Velazquez, D., Tonda, A., Rodriguez-Guerra, I., Kraneveld, A.D., Lopez-Rincon, A. (2023). Multi-objective Evolutionary Discretization of Gene Expression Profiles: Application to COVID-19 Severity Prediction. In: Correia, J., Smith, S., Qaddoura, R. (eds) Applications of Evolutionary Computation. EvoApplications 2023. Lecture Notes in Computer Science, vol 13989. Springer, Cham. https://doi.org/10.1007/978-3-031-30229-9_45

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  • DOI: https://doi.org/10.1007/978-3-031-30229-9_45

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