Statistical and Machine-Learning Analyses in Nutritional Genomics Studies.

ABSTRACT: 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 algorith

SUBMITTER: Khorraminezhad L 

PROVIDER: S-EPMC7602401 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature