<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Burton-Pimentel KJ</submitter><funding>Joint Programming Initiative A healthy diet for a healthy life</funding><funding>National Children&amp;apos;s Research Centre</funding><pagination>e2000647</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC8221028</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>65(4)</volume><pubmed_abstract>&lt;h4>Scope&lt;/h4>Combining different "omics" data types in a single, integrated analysis may better characterize the effects of diet on human health.&lt;h4>Methods and results&lt;/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</pubmed_abstract><journal>Molecular nutrition &amp; food research</journal><pubmed_title>Discriminating Dietary Responses by Combining Transcriptomics and Metabolomics Data in Nutrition Intervention Studies.</pubmed_title><pmcid>PMC8221028</pmcid><funding_grant_id>14/JP‐HDHL/B3076</funding_grant_id><funding_grant_id>B/11/1</funding_grant_id><pubmed_authors>Afman LA</pubmed_authors><pubmed_authors>Hughes M</pubmed_authors><pubmed_authors>Pimentel G</pubmed_authors><pubmed_authors>Ibberson M</pubmed_authors><pubmed_authors>Vergeres G</pubmed_authors><pubmed_authors>Michielsen CC</pubmed_authors><pubmed_authors>Vionnet N</pubmed_authors><pubmed_authors>Roche HM</pubmed_authors><pubmed_authors>Burton-Pimentel KJ</pubmed_authors><pubmed_authors>Fatima A</pubmed_authors><pubmed_authors>Brennan L</pubmed_authors></additional><is_claimable>false</is_claimable><name>Discriminating Dietary Responses by Combining Transcriptomics and Metabolomics Data in Nutrition Intervention Studies.</name><description>&lt;h4>Scope&lt;/h4>Combining different "omics" data types in a single, integrated analysis may better characterize the effects of diet on human health.&lt;h4>Methods and results&lt;/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</description><dates><release>2021-01-01T00:00:00Z</release><publication>2021 Feb</publication><modification>2026-05-08T17:47:41.758Z</modification><creation>2022-02-10T17:15:37.974Z</creation></dates><accession>S-EPMC8221028</accession><cross_references><pubmed>33325641</pubmed><doi>10.1002/mnfr.202000647</doi></cross_references></HashMap>