Our main research areas comprise some of the most interesting interdisciplinary aspects across Medical Information Sciences.
Recent advances in high-throughput technologies have led to an exponential increase in biological data (such as genomic, epigenomic and proteomic data). To find meaningful insights in such large data collections, efficient statistical learning methods are needed.
We are interested in developing and applying new Machine Learning / statistical learning methods to solving biomedical problems and answering new biomedical questions. Besides our previous focus on proteomic data, our aim is to provide informed, state-of-the-art solutions for treatments of HIV, HCV, cancer, malaria, influenza and other diseases based on clinical, genomic and epigenomic data.
All those topics can also be subsumed under the category “Machine Learning for Precision Medicine”.