Applying Machine-Learning to Human Gastrointestinal Microbial Species to Predict Dietary Intake (P20-040-19)

OBJECTIVES: To better understand host-microbe interactions, a more computationally intensive, multivariate, machine learning approach must be utilized. Accordingly, we aimed to identify biomarkers with high predictive accuracy for dietary intake. METHODS: Data were aggregated from five randomized, c...

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Detalhes bibliográficos
Publicado no:Curr Dev Nutr
Main Authors: Shinn, Leila, Li, Yutong, Zhu, Ruoqing, Mansharamani, Aditya, Auvil, Loretta, Welge, Michael, Bushell, Colleen, Khan, Naiman, Charron, Craig, Novotny, Janet, Baer, David, Holscher, Hannah
Formato: Artigo
Idioma:Inglês
Publicado em: Oxford University Press 2019
Assuntos:
Nutritional Microbiology
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC6574017/
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1093/cdn/nzz040.P20-040-19
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