{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Burton-Pimentel KJ"],"funding":["Joint Programming Initiative A healthy diet for a healthy life","National Children&apos;s Research Centre"],"pagination":["e2000647"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8221028"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["65(4)"],"pubmed_abstract":["<h4>Scope</h4>Combining different \"omics\" data types in a single, integrated analysis may better characterize the effects of diet on human health.<h4>Methods and results</h4>The performance of two data integration tools, similarity network fusion tool (SNFtool) and Data Integration Analysis for Biomarker discovery using Latent variable approaches for \"Omics\" (DIABLO; MixOmics), in discriminating responses to diet and metabolic phenotypes is investigated by combining transcriptomics and metabolomics datasets from three human intervention studies: a postprandial crossover study testing dairy foods (n = 7; study 1), a postprandial challenge study comparing obese and non-obese subjects (n = 13; study 2); and an 8-week parallel intervention study that assessed three diets with variable lipid co"],"journal":["Molecular nutrition & food research"],"pubmed_title":["Discriminating Dietary Responses by Combining Transcriptomics and Metabolomics Data in Nutrition Intervention Studies."],"pmcid":["PMC8221028"],"funding_grant_id":["14/JP‐HDHL/B3076","B/11/1"],"pubmed_authors":["Afman LA","Hughes M","Pimentel G","Ibberson M","Vergeres G","Michielsen CC","Vionnet N","Roche HM","Burton-Pimentel KJ","Fatima A","Brennan L"],"additional_accession":[]},"is_claimable":false,"name":"Discriminating Dietary Responses by Combining Transcriptomics and Metabolomics Data in Nutrition Intervention Studies.","description":"<h4>Scope</h4>Combining different \"omics\" data types in a single, integrated analysis may better characterize the effects of diet on human health.<h4>Methods and results</h4>The performance of two data integration tools, similarity network fusion tool (SNFtool) and Data Integration Analysis for Biomarker discovery using Latent variable approaches for \"Omics\" (DIABLO; MixOmics), in discriminating responses to diet and metabolic phenotypes is investigated by combining transcriptomics and metabolomics datasets from three human intervention studies: a postprandial crossover study testing dairy foods (n = 7; study 1), a postprandial challenge study comparing obese and non-obese subjects (n = 13; study 2); and an 8-week parallel intervention study that assessed three diets with variable lipid co","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Feb","modification":"2026-05-08T17:47:41.758Z","creation":"2022-02-10T17:15:37.974Z"},"accession":"S-EPMC8221028","cross_references":{"pubmed":["33325641"],"doi":["10.1002/mnfr.202000647"]}}