The research interests of my group, the Predictive Medicine and Analytics Laboratory, are in the development of high-dimensional Machine Learning and Statistics methods that can be used for the analysis of high-throughput genomics data. The data can come from gene expression, methylation, copy number variation, DNA-seq or RNA-seq experiments, e.g., generated from next-generation sequencing technologies. We are interested in basic biological questions relating to E.coli, S.cerevisiae and Human, but our particular focus is on the understanding of biomedical and clinical questions of complex disorders. We have experience working with data from various cancer types, e.g., breast cancer, colon cancer, bladder cancer, prostate cancer, kidney cancer and lymphoma and but we are also involved in studies of cystic fibrosis, asthma and diabetes. In addition, we are working on problems studying the pluripotency of Human stem cells and regulatory mechanisms of memory T-Cells in mice. All of these studies are conducted in close collaboration with biologists and clinicians.
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