Abstract
In the field of precision nutrition, predicting high dimensional metabolic response to diet and identifying groups of differential responders are two highly desirable steps towards eveloping tailored dietary strategies. However, proper data analysis tools are currently lacking, especially for complex settings such as crossover studies. Current methods of analysis often rely on matrix or tensor decompositions, which are well suited for identifying differential responders but lacking in predictive power, or on dynamical systems modelling, which may be used for prediction but typically requires detailed mechanistic knowledge of the system under study.