Metabolomics studies, particularly liquid chromatography mass spectrometry (LC-MS)-based untargeted approaches, generate information-rich, high-dimensional, and complex datasets that remain challenging to handle and fully exploit . Thus, dedicated modeling algorithms, able to cope with the inherent complexity of these metabolomic datasets are mandatory for extracting relevant information. Various chemometric and bioinformatics tools and resources have been developed, and are utilized for this purpose, thereby integrating computer science, mathematics, and statistics . However, despite the remarkable progress in the development of tools and algorithms, as presented in the recent review , the exhaustive extraction of information from these metabolomic datasets is still a non-trivial undertaking .