Multimodal Biomedical data analysis

This research axis focuses on the study of the relationship between complex biomedical data, such as phenotypes quantified by neuroimaging (such as atrophy, or connectivity) and individual’s -omics profile (e.g. large genetic arrays). To solve this problem, we develop efficient and flexible statistical models for studying multivariate patterns of associations between mutimodal biomedical data.

Selected software and publications:

• Genome to Phenome Sparse Bayesian Regression; Deprez et al, Front Mol Med, 2022
• Functional Prioritization for Imaging Genetics Analysis in Alzheimer’s Disease, Lorenzi et al, PNAS, 2018