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Human Multi-omics Data Pre-processing for Predictive Purposes Using Machine Learning: A Case Study in Childhood Obesity

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Bioinformatics and Biomedical Engineering (IWBBIO 2022)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13347))

Abstract

The Machine Learning applications in the medical field using omics data are countless and promising, highlighting the possibility of creating long-term predictive models for highly prevalent diseases. Nevertheless, to take advantage of the virtues of omics data and machine learning tools, we first need to perform adequate data pre-processing just as taking some considerations for the constructions of the models. The present paper is an example of how to face the main challenges encountered when constructing machine learning predictive models with multi-omics human data. Some topics covered in this work include a description of the main particularities of each omics data layer, the most appropriate pre-processing approaches for each source, and a collection of good practices and tips for applying machine learning to this kind of data with predictive purposes. Using real data examples (blood samples), we illustrate how some of the key issues are addressed in this kind of research (technical noise, biological heterogeneity, class imbalance, high dimensionality, and presence of missing values, among others). Additionally, we set the basis for future work incorporating some proposals to improve models, arguing their need according to encountered insights.

Supported organization in part by ERDF/Regional Government of Andalusia/Ministry of Economic Transformation, Industry, Knowledge and Universities (grant numbers P18-RT-2248 and B-CTS-536-UGR20) and by the ERDF/Health Institute Carlos III/Spanish Ministry of Science, Innovation and Universities (grant number PI20/00711, PI16/00871 and PI20/00563).

Á. Torres-Martos and A. Anguita-Ruiz—Equal contributors.

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Authors and Affiliations

  1. Department of Biochemistry and Molecular Biology II, School of Pharmacy, University of Granada, 18071, Granada, Spain

    Álvaro Torres-Martos, Augusto Anguita-Ruiz, Mireia Bustos-Aibar, Sofia Cámara-Sánchez & Concepción M. Aguilera

  2. Institute of Nutrition and Food Technology “José Mataix” Center of Biomedical Research, Instituto de Investigación Biosanitaria IBS.GRANADA, Complejo Hospitalario Universitario de Granada, University of Granada, Avda. del Conocimiento s/n., 18016, 18012, Granada, Spain

    Augusto Anguita-Ruiz & Concepción M. Aguilera

  3. CIBEROBN (CIBER Physiopathology of Obesity and Nutrition), Instituto de Salud Carlos III, 28029, Madrid, Spain

    Augusto Anguita-Ruiz & Concepción M. Aguilera

  4. Barcelona Institute for Global Health (ISGlobal), Doctor Aiguader 88, 08003, Barcelona, Spain

    Augusto Anguita-Ruiz

  5. Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18071, Granada, Spain

    Rafael Alcalá & Jesús Alcalá-Fdez

Authors
  1. Álvaro Torres-Martos
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  2. Augusto Anguita-Ruiz
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  3. Mireia Bustos-Aibar
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  4. Sofia Cámara-Sánchez
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  5. Rafael Alcalá
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  6. Concepción M. Aguilera
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  7. Jesús Alcalá-Fdez
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Corresponding authors

Correspondence to Álvaro Torres-Martos , Augusto Anguita-Ruiz , Mireia Bustos-Aibar , Sofia Cámara-Sánchez or Concepción M. Aguilera .

Editor information

Editors and Affiliations

  1. Marcelina Siebold Guest Relations Dept., University of Granada, Granada, Spain

    Ignacio Rojas

  2. Faculty of Sciences, University of Granada, Granada, Spain

    Olga Valenzuela

  3. ETSIIT. CITIC-UGR, University of Granada, Granada, Spain

    Fernando Rojas

  4. ETSIIT, University of Granada, Granada, Spain

    Luis Javier Herrera

  5. University of Granada, Granada, Spain

    Francisco Ortuño

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Torres-Martos, Á. et al. (2022). Human Multi-omics Data Pre-processing for Predictive Purposes Using Machine Learning: A Case Study in Childhood Obesity. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L.J., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2022. Lecture Notes in Computer Science(), vol 13347. Springer, Cham. https://doi.org/10.1007/978-3-031-07802-6_31

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  • DOI: https://doi.org/10.1007/978-3-031-07802-6_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-07801-9

  • Online ISBN: 978-3-031-07802-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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