Recent advances in statistics and computational techniques as well as experimental technologies are transforming how we generate and analyze data. This big data has big potential. But that potential can only be fully realized through collaboration between researchers with complementary skill sets, approaches and resources.
The Translational Data Science IRC harnesses these advances and expertise to spur innovation and open up new avenues for preventing and treating cancer and related diseases. The TDS IRC builds on recent data-based advances in the biomedical sciences, including technologies that are driving the generation of massive datasets, the exponential growth of publicly available data, and revolutionary approaches in statistics, machine learning and artificial intelligence that are changing data analysis. Researchers in the TDS IRC develop statistical methods and computational tools for managing and analyzing high-throughput, high-dimensional data.
Dr. Raphael Gottardo is scientific director of the TDS. His vision and leadership have helped position Fred Hutch as an incubator of new computational tools and methodologies and as a translator of data-driven discoveries into improved patient care.
The goal of the TDS IRC is to infuse data science throughout the bench-to-bedside discovery cycle and to fuel new research opportunities by fostering increased interaction between Fred Hutch’s experimental and clinical researchers and their quantitative and computational science colleagues — as well as with external groups.
The IIRC creates partnerships that draw on our unique expertise to accelerate the discovery of new immune-based therapies.Learn more
The PAM IRC is a center-wide collaboration whose goal is to understand and prevent cancer caused by pathogens.Learn more
A new generation of computational technologies is revolutionizing cancer research. At Fred Hutch, we invest heavily in data science infrastructure and expertise so we can get to new discoveries faster using the vast and growing amounts of available genetic and health data.