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Analyzing Feature Importance for Metabolomics Using Genetic Programming

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Genetic Programming (EuroGP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10781))

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Abstract

The emerging and fast-developing field of metabolomics examines the abundance of small-molecule metabolites in body fluids to study the cellular processes related to how the human body responds to genetic and environmental perturbations. Considering the complexity of metabolism, metabolites and their represented cellular processes can correlate and synergistically contribute to a phenotypic status. Genetic programming (GP) provides advanced analytical instruments for the investigation of multifactorial causes of metabolic diseases. In this article, we analyzed a population-based metabolomics dataset on osteoarthritis (OA) and developed a Linear GP (LGP) algorithm to search classification models that can best predict the disease outcome, as well as to identify the most important metabolic markers associated with the disease. The LGP algorithm was able to evolve prediction models with high accuracies especially with a more focused search using a reduced feature set that only includes potentially relevant metabolites. We also identified a set of key metabolic markers that may improve our understanding of the biochemistry and pathogenesis of the disease.

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Acknowledgments

This research was supported by Newfoundland and Labrador Research and Development Corporation (RDC) Ignite Grant 5404.1942.101 and the Natural Science and Engineering Research Council (NSERC) of Canada Discovery Grant RGPIN-2016-04699 to TH. GZ acknowledges grants from Canadian Institute of Health Research (CIHR), Newfoundland and Labrador Research and Development Corporation (RDC) and Memorial University. We thank all the study participants who made this study possible and all the Operation Room staff at Eastern Health General Hospital and St. Clare’s Hospital who helped for collecting samples.

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Authors and Affiliations

  1. Department of Computer Science, Memorial University, St. John’s, NL, A1B 3X5, Canada

    Ting Hu & Karoliina Oksanen

  2. Faculty of Medicine, Memorial University, St. John’s, NL, A1B 3V6, Canada

    Weidong Zhang, Edward Randell, Andrew Furey & Guangju Zhai

  3. School of Pharmaceutical Sciences, Jilin University, Jilin, Changchun, China

    Weidong Zhang

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  1. Ting Hu
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  2. Karoliina Oksanen
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  3. Weidong Zhang
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  4. Edward Randell
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  5. Andrew Furey
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  6. Guangju Zhai
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Corresponding author

Correspondence to Ting Hu .

Editor information

Editors and Affiliations

  1. Universidade Nova de Lisboa, Lisbon, Portugal

    Mauro Castelli

  2. Brno University of Technology, Brno, Czech Republic

    Lukas Sekanina

  3. Victoria University of Wellington, Wellington, New Zealand

    Mengjie Zhang

  4. University of Parma, Parma, Italy

    Stefano Cagnoni

  5. University of Cádiz, Cádiz, Spain

    Pablo García-Sánchez

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Hu, T., Oksanen, K., Zhang, W., Randell, E., Furey, A., Zhai, G. (2018). Analyzing Feature Importance for Metabolomics Using Genetic Programming. In: Castelli, M., Sekanina, L., Zhang, M., Cagnoni, S., García-Sánchez, P. (eds) Genetic Programming. EuroGP 2018. Lecture Notes in Computer Science(), vol 10781. Springer, Cham. https://doi.org/10.1007/978-3-319-77553-1_5

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  • DOI: https://doi.org/10.1007/978-3-319-77553-1_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77552-4

  • Online ISBN: 978-3-319-77553-1

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