Modelling Asthma Patients’ Responsiveness to Treatment Using Feature Selection and Evolutionary Computation
Lopez-Rincon, Alejandro; Roozendaal, Daphne S.; Spierenburg, Hilde M.; Holm, Asta L.; Metcalf, Renee; Perez-Pardo, Paula; Kraneveld, Aletta D.; Tonda, Alberto; Castillo, Pedro A.; Jiménez Laredo, Juan Luis
(2021)
Applications of Evolutionary Computation - 24th International Conference, EvoApplications 2021, Held as Part of EvoStar 2021, Proceedings, volume 12694, pp. 359 - 372
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 12694, pp. 359 - 372
24th International Conference on the Applications of Evolutionary Computation, EvoApplications 2021 held as Part of EvoStar 2021, volume 12694, pp. 359 - 372
(Part of book)
Abstract
For several medical treatments, it is possible to observe transcriptional variations in gene expressions between responders and non-responders. Modelling the correlation between such variations and the patient’s response to drugs as a system of Ordinary Differential Equations could be invaluable to improve the efficacy of treatments and would represent an
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important step towards personalized medicine. Two main obstacles lie on this path: (i) the number of genes is too large to straightforwardly analyze their interactions; (ii) defining the correct parameters for the mathematical models of gene interaction is a complex optimization problem, even when a limited number of genes is involved. In this paper, we propose a novel approach to creating mathematical models able to explain patients’ response to treatment from transcriptional variations. The approach is based on: (i) a feature selection algorithm, set to identify a minimal set of gene expressions that are highly correlated with treatment outcome, (ii) a state-of-the-art evolutionary optimizer, Covariance Matrix Adaptation Evolution Strategy, applied to finding the parameters of the mathematical model characterizing the relationship between gene expressions and patient responsiveness. The proposed methodology is tested on real-world data describing responsiveness of asthma patients to Omalizumab, a humanized monoclonal antibody that binds to immunoglobulin E. In this case study, the presented approach is shown able to identify 5 genes (out of 28,402) that are transcriptionally relevant to predict treatment outcomes, and to deliver a compact mathematical model that is able to explain the interaction between the different genes involved.
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Keywords: Asthma, Evolutionary computation, Machine learning, Mathematical model, Omalizumab, Theoretical Computer Science, Computer Science(all)
ISSN: 0302-9743
ISBN: 9783030726980
Publisher: Springer Science and Business Media Deutschland GmbH
Note: Publisher Copyright: © 2021, Springer Nature Switzerland AG. DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
(Peer reviewed)