Assistant Professor of Medicine (Biomedical Informatics) and of Biomedical Data Science
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 I pioneered data fusion work using Bayesian and kernel methods studying breast and ovarian cancer. My 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.