Distinguished Speakers Seminar Series
Predicting Genetic Variants that Alter 3D Genome Folding in Cancer and Developmental Disorders
Seminar Series Details:
About Katie Pollard (UCSF)
Katherine S. Pollard
Director, Gladstone Institute of Data Science & Biotechnology
Professor, University of California San Francisco
Investigator, Chan Zuckerberg Biohub
About this Class
The role of computational science in biomedical research has typically been downstream of experiments, where it plays important roles in signal processing, data integration, pattern detection, and hypothesis testing. But this is changing, and predictive models are now being used to generate and test hypotheses in silico. In this talk, Dr. Pollard will share examples from human genetics, where they have built deep learning models of 3D chromatin interactions that take only sequence as input and then used them to interpret disease variants. This strategy leads to causal hypotheses and enables them to prioritize variants with predicted functional effects. Experiments designed using model outputs are accelerating the rate of discoveries, shedding light on genetic mechanisms in cancer and developmental disorders. This prediction-first strategy exemplifies Dr. Pollard's vision for a more proactive, rather than reactive, role for computational science in biomedical research.