Statistical and Machine-Learning Analyses in Nutritional Genomics Studies.
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 nutr...
| Published in: | Nutrients Vol. 12; no. 10; pp. 3140 - 3141 |
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| Main Authors: | , , , , |
| Format: | review tables/charts Journal Article |
| Published: |
MDPI
Oct2020
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=146658610&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 146658610 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726643 B0TT jtl: Nutrients issn: 20726643 maglogo: N pubinfo: dt: Oct2020 vid: 12 iid: 10 pid: 97109 pub: MDPI artinfo: ui: 146658610 146658610 146658610 10.3390/nu12103140 146658610 ppf: 3140 ppct: 1 formats: tig: atl: Statistical and Machine-Learning Analyses in Nutritional Genomics Studies. aug: au: Khorraminezhad, Leila Leclercq, Mickael Droit, Arnaud Bilodeau, Jean-François Rudkowska, Iwona affil: Endocrinology and Nephrology Unit, CHU de Québec-Laval University Research Center, Quebec (PQ), QC G1V 4G2, Canada sug: subj: Data Analysis, Statistical Machine Learning Nutrigenomics Nutrition Data Mining Food Intake Data Management Research, Medical ab: 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. pubtype: Academic Journal doctype: review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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