A comparative study of supervised and unsupervised machine learning algorithms applied to human microbiome.

Journal: La Clinica terapeutica
Published Date: Jan 1, 2024

Abstract

BACKGROUND: The human microbiome, consisting of diverse bacte-rial, fungal, protozoan and viral species, exerts a profound influence on various physiological processes and disease susceptibility. However, the complexity of microbiome data has presented significant challenges in the analysis and interpretation of these intricate datasets, leading to the development of specialized software that employs machine learning algorithms for these aims.

Authors

  • E Kalluçi
    Department of Applied Mathematics, Faculty of Natural Sciences, University of Tirana, Tirana, Albania.
  • B Preni
    Department of Mathemat-ics, Faculty of Engineering Mathematics and Engineering Physics, Polytechnic University of Tirana, Tirana, Albania.
  • X Dhamo
    Department of Applied Mathematics, Faculty of Natural Sciences, University of Tirana, Tirana, Albania.
  • E Noka
    Department of Applied Mathematics, Faculty of Natural Sciences, University of Tirana, Tirana, Albania.
  • S Bardhi
    Department of Applied Statistics and Informatics, University of Tirana, Tirana, Albania.
  • A Macchia
    MAGI'S LAB, Rovereto (TN), Italy.
  • G Bonetti
    MAGI'S LAB, Rovereto (TN), Italy.
  • K Dhuli
    MAGI'S LAB, Rovereto (TN), Italy.
  • K Donato
    MAGI EUREGIO, Bolzano, Italy.
  • M Bertelli
  • L J M Zambrano
    Computational Biology Group, Precision Nutrition and Cancer Research Program, IMDEA Food Institute, Madrid, Spain.
  • S Janaqi
    EuroMov Digital Health in Motion, University of Montpellier IMT Mines Ales, France.

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