Metabolomics is one way of studying metabolism. The presence of certain metabolites, or the breakdown of metabolic pathways can serve as indicators of a patient's health. They can serve as markers for certain diseases such as cancers, or provide information on the quality of an individual's diet. Untargeted metabolomics acquisition methods produce large data matrices. The aim is to develop machine learning methods specifically suited to handle high dimensional data sets. For example models based on decision rules.
The purpose of these models being the search for biomarkers, they must be sparse in order to be able to be interpreted by a human expert. We also try to develop new approaches to better interpret some existing and efficient models. Interpretability is essential in the application of machine learning to health. Models cannot be diagnostic black boxes but rather analytical tools available to experts to better understand human metabolism.