Abstract: The advancement of technologies for high-throughput collection of personal data, including environmental, lifestyle, clinical and biomedical data, has inadvertently transformed biology and medicine. Integrating and co-analyzing these different data streams has become the research bottleneck and, in all likelihood, will be a central research topic for the next decade. Machine Learning has shown promise addressing biomedical and clinical problems, especially regarding classification. Causal graphical models allow for inferring potential cause-effect relations on the whole dataset and are by nature interpretable. My group has worked in extending causal learning framework to mixed data types, incorporating prior information and learning causal graphs with latent confounders. In this talk, I will present an overview of causal inference and some of our results on applications on important biomedical and clinical questions in cancer diagnosis and therapy.

Speaker: Dr Takis Benos

RSVP for the Zoom meeting information: https://pitt.co1.qualtrics.com/jfe/form/SV_7PM9H7QUWOg6wLA