Abstract
The overarching goal of Precision Nutrition modeling is to identify factors that predict the individual response to different diets with the aim of using these factors to design personalized diets. Recent Precision Nutrition studies have included multiple sources of big data, such as data from the gut microbiome and metabolomics. Adding novel sources of data can elucidate factors that may contribute to the variations in individual responses that were previously unknown. However, including big data with traditional data sources (surveys, clinical measurements) also increases the complexity of harmonizing and integrating data and requires best practices carefully executed within a modeling pipeline.
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