The Koo Lab studies the functional impact of genomic mutations through a computational lens using data-driven machine learning solutions. We are broadly interested in applications for studying gene regulation and protein (dys)function. Our approach develops methods to interpret high-performing deep learning models to distill knowledge that they learn from big, noisy biological sequence data. Our goal is to elucidate biological mechanisms that underlie sequence-function relationships, with a broader aim of advancing precision medicine for complex diseases, including cancer. We are part of the Simons Center for Quantitative Biology and the Cancer Center at Cold Spring Harbor Laboratory.