The Gevaert lab focuses on multi-scale data fusion in oncology: the development of machine learning methods for biomedical decision support using multi-scale biomedical data. Previously Professor Gevaert pioneered data fusion work using Bayesian and kernel methods studying breast and ovarian cancer. His 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, his 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.