Dr. Olivier Gevaert research focuses on using advanced machine learning methods to integrate molecular biology data of cancer patients. These data sources are often called omics (e.g. genomics, transcriptomics, proteomics, ...). In addition, I'm also investigating strategies to couple these omics data sources with medical imaging data (e.g. MRI, PET, CT, ...). I primarily use methods based on regularized linear regression including different types of regularization such as lasso, ridge regression, elastic net and combinations or extensions of these. I apply these methods primarily in oncology. I’m currently studying data integration of breast cancer, lung cancer and T-cell acute lymphoblastic leukemia. Both to improve current understanding of pathways that drive oncogenesis or to improve diagnosis, prognosis or therapy response prediction. In the past I worked primarily to study ovarian cancer, hepatocellular carcinoma and gliomas.