首页|New Machine Learning Data Has Been Reported by a Researcher at University of Not tingham (Machine Learning Metabolomics Profiling of Dietary Interventions from a Six-Week Randomised Trial)
New Machine Learning Data Has Been Reported by a Researcher at University of Not tingham (Machine Learning Metabolomics Profiling of Dietary Interventions from a Six-Week Randomised Trial)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on artificial intelligen ce have been presented. According to news reporting out of Nottingham, United Ki ngdom, by NewsRx editors, research stated, "Metabolomics can uncover physiologic al responses to prebiotic fibre and omega-3 fatty acid supplements with known he alth benefits and identify response-specific metabolites." Our news journalists obtained a quote from the research from University of Notti ngham: "We profiled 534 stool and 799 serum metabolites in 64 healthy adults fol lowing a 6-week randomised trial comparing daily omega-3 versus inulin supplemen tation. Elastic net regressions were used to separately identify the serum and s tool metabolites whose change in concentration discriminated between the two typ es of supplementations. Random forest was used to explore the gut microbiome's c ontribution to the levels of the identified metabolites from matching stool samp les. Changes in serum 3-carboxy-4-methyl-5-propyl- 2-furanpropanoate and indolepr oprionate levels accurately discriminated between fibre and omega-3 (area under the curve (AUC) = 0.87 [95% confidence interval (CI): 0.63-0.99]), while stool eicosapentaenoate indicated o mega-3 supplementation (AUC = 0.86 [95% CI: 0.6 4-0.98]). Univariate analysis also showed significant increas es in indoleproprionate with fibre, 3-carboxy-4-methyl-5-propyl-2-furanpropanoat e, and eicosapentaenoate with omega-3. Out of these, only the change in indolepr oprionate was partly explained by changes in the gut microbiome composition (AUC = 0.61 [95% CI: 0.58-0.64] and Rho = 0.21 [95% CI: 0.08-0.34] ) and positively correlated with the increase in the abundance of the genus Copr ococcus (p = 0.005)."
University of NottinghamNottinghamUn ited KingdomEuropeCyborgsEmerging TechnologiesMachine Learning
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