Authors

Leila Khorraminezhad, Mickael Leclercq, Arnaud Droit, Jean-François Bilodeau and Iwona Rudkowska

Abstract

Machine Learning (ML) can be used for data mining, sample clustering, and classification to produce predictive models and algorithms for integration of multi-OMICs in response to dietary intake. The objective of this review was to investigate the strategies used for the analysis of multi-OMICs data in nutrition studies.

Importance of the article for the Red Tecnomifood

Diet influences several omic layers, therefore their integrated analysis provides information on the metabolic mechanisms by which food modifies health. AI emerges as a possibility to complement traditional statistics and obtain more precise information on nutrition-health-disease relationships.