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A New Algorithm for Multivariate Genome Wide Association Studies Based on Differential Evolution and Extreme Learning Machines

Author

Listed:
  • David Álvarez Gutiérrez

    (SERGAS, UAP CS, 27720 A Pontenova, Spain)

  • Fernando Sánchez Lasheras

    (Department of Mathematics, University of Oviedo, 33007 Oviedo, Spain
    Instituto Universitario de Ciencias y Tecnologías Espaciales de Asturias (ICTEA), University of Oviedo, 33004 Oviedo, Spain)

  • Vicente Martín Sánchez

    (CIBERESP, University of Leon, Vegazana Campus, 24400 Leon, Spain)

  • Sergio Luis Suárez Gómez

    (Department of Mathematics, University of Oviedo, 33007 Oviedo, Spain
    Instituto Universitario de Ciencias y Tecnologías Espaciales de Asturias (ICTEA), University of Oviedo, 33004 Oviedo, Spain)

  • Víctor Moreno

    (Oncology Data Analytics Programme, Catalan Institute of Oncology (ICO), Hospitalet de Llobregat, 08907 Barcelona, Spain
    Colorectal Cancer Research Group, ONCOBELL Programme, Institut d’Investigació Biomèdica de Bellvitge (IDIBELL), Hospitalet de Llobregat, 08907 Barcelona, Spain
    Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain
    Department of Clinical Sciences, Faculty of Medicine and Health Sciences, University of Barcelona, 08907 Barcelona, Spain)

  • Ferrán Moratalla-Navarro

    (Oncology Data Analytics Programme, Catalan Institute of Oncology (ICO), Hospitalet de Llobregat, 08907 Barcelona, Spain
    Colorectal Cancer Research Group, ONCOBELL Programme, Institut d’Investigació Biomèdica de Bellvitge (IDIBELL), Hospitalet de Llobregat, 08907 Barcelona, Spain
    Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain
    Department of Clinical Sciences, Faculty of Medicine and Health Sciences, University of Barcelona, 08907 Barcelona, Spain)

  • Antonio José Molina de la Torre

    (IBIOMED, University of Leon, Vegazana Campus, 24400 Leon, Spain)

Abstract

Genome-wide association studies (GWAS) are observational studies of a large set of genetic variants, whose aim is to find those that are linked to a certain trait or illness. Due to the multivariate nature of these kinds of studies, machine learning methodologies have been already applied in them, showing good performance. This work presents a new methodology for GWAS that makes use of extreme learning machines and differential evolution. The proposed methodology was tested with the help of the genetic information (370,750 single-nucleotide polymorphisms) of 2049 individuals, 1076 of whom suffer from colorectal cancer. The possible relationship of 10 different pathways with this illness was tested. The results achieved showed that the proposed methodology is suitable for detecting relevant pathways for the trait under analysis with a lower computational cost than other machine learning methodologies previously proposed.

Suggested Citation

  • David Álvarez Gutiérrez & Fernando Sánchez Lasheras & Vicente Martín Sánchez & Sergio Luis Suárez Gómez & Víctor Moreno & Ferrán Moratalla-Navarro & Antonio José Molina de la Torre, 2022. "A New Algorithm for Multivariate Genome Wide Association Studies Based on Differential Evolution and Extreme Learning Machines," Mathematics, MDPI, vol. 10(7), pages 1-21, March.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:7:p:1024-:d:777377
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    References listed on IDEAS

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    1. Gyanendra Prasad Joshi & Fayadh Alenezi & Gopalakrishnan Thirumoorthy & Ashit Kumar Dutta & Jinsang You, 2021. "Ensemble of Deep Learning-Based Multimodal Remote Sensing Image Classification Model on Unmanned Aerial Vehicle Networks," Mathematics, MDPI, vol. 9(22), pages 1-17, November.
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