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Open AccessReview

Statistical and Machine-Learning Analyses in Nutritional Genomics Studies

1
Endocrinology and Nephrology Unit, CHU de Québec-Laval University Research Center, Quebec (PQ), QC G1V 4G2, Canada
2
Department of Molecular Medicine, Faculty of Medicine, Laval University, Quebec (PQ), QC G1V 0A6, Canada
3
Department of Medicine, Faculty of Medicine, Laval University, Quebec (PQ), QC G1V 0A6, Canada
4
Department of Kinesiology, Faculty of Medicine, Laval University, Quebec (PQ), QC G1V 0A6, Canada
*
Author to whom correspondence should be addressed.
Nutrients 2020, 12(10), 3140; https://doi.org/10.3390/nu12103140
Received: 11 September 2020 / Revised: 8 October 2020 / Accepted: 10 October 2020 / Published: 14 October 2020
(This article belongs to the Special Issue Genomics and Personalized Nutrition)
Nutritional compounds may have an influence on different OMICs levels, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and metagenomics. The integration of OMICs data is challenging but may provide new knowledge to explain the mechanisms involved in the metabolism of nutrients and diseases. Traditional statistical analyses play an important role in description and data association; however, these statistical procedures are not sufficiently enough powered to interpret the large integrated multiple OMICs (multi-OMICS) datasets. Machine learning (ML) approaches can play a major role in the interpretation of multi-OMICS in nutrition research. Specifically, 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. Sixteen recent studies aimed to understand the association between dietary intake and multi-OMICs data are summarized. Multivariate analysis in multi-OMICs nutrition studies is used more commonly for analyses. Overall, as nutrition research incorporated multi-OMICs data, the use of novel approaches of analysis such as ML needs to complement the traditional statistical analyses to fully explain the impact of nutrition on health and disease. View Full-Text
Keywords: genomics; multi-OMICS; machine learning; data integration; nutrition genomics; multi-OMICS; machine learning; data integration; nutrition
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MDPI and ACS Style

Khorraminezhad, L.; Leclercq, M.; Droit, A.; Bilodeau, J.-F.; Rudkowska, I. Statistical and Machine-Learning Analyses in Nutritional Genomics Studies. Nutrients 2020, 12, 3140. https://doi.org/10.3390/nu12103140

AMA Style

Khorraminezhad L, Leclercq M, Droit A, Bilodeau J-F, Rudkowska I. Statistical and Machine-Learning Analyses in Nutritional Genomics Studies. Nutrients. 2020; 12(10):3140. https://doi.org/10.3390/nu12103140

Chicago/Turabian Style

Khorraminezhad, Leila; Leclercq, Mickael; Droit, Arnaud; Bilodeau, Jean-François; Rudkowska, Iwona. 2020. "Statistical and Machine-Learning Analyses in Nutritional Genomics Studies" Nutrients 12, no. 10: 3140. https://doi.org/10.3390/nu12103140

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