The Gevaert lab focuses on biomedical data fusion of complex diseases with a particular focus on oncology and cardiovascular diases. We develop novel machine learning approaches that digest multi-omics, multi-modal or multi-scale data. Previously we pioneered data fusion work using Bayesian and kernel methods studying breast and ovarian cancer. Subsequent work concerned the development of methods for multi-omics data fusion. This resulted in the development of MethylMix, to identify differentially methylated genes, and AMARETTO, a computational method to integrate DNA methylation, copy number and gene expression data to identify cancer modules. Additionally, my lab focuses on linking molecular data with cellular and tissue-level phenotypes. This led to key contributions in the field of imaging genomics/radiogenomics involving work in lung cancer and brain tumors. Our work in imaging genomics is focused on developing a framework for non-invasive personalized medicine. In summary, my lab has an interdisciplinary focus on developing novel algorithms for multi-scale biomedical data fusion.