University of Ottawa

Published purpose

Clinical measurements have seen major improvements with increasingly high-quality data becoming available. Linking these data through hybrid machine learning and knowledge driven methods with molecular data provided by metabolomics and lipidomics is expected to lead to tangible clinical solutions in design of novel diagnostic, theragnostic and therapy avenues. Design of analytical AI and ML methods linking metabolome and lipidome with for example MRI or EEG is expected to provide an avenue for the understanding and design of innovative diagnostic and therapeutic modalities. The project aims to develop novel integrated ML driven methods for deriving information from clinical, structural measures such as EEG and MRI that can be directly linked with related molecular omics measures, specifically metabolomics and lipidomics in order to provide improved design of diagnostic and prognostic methods. Focus will be on application in neurodegenerative diseases of aging