Biomedical Data Science Seminar Series, BRB Gaulton Auditorium

Title: Making clinical trials and precision medicine more effective and diverse with real-world clinico-genomics data.

This week hosted by IBI – invited by Junhyong Kim

Abstract: I will discuss how to leverage large-scale EHR data linked with tumor mutation profiles to emulate cancer clinical trials in silico. This approach helps us to design real trials, making them more inclusive and efficient (Liu et al. Nature 2021). It also helps to identify a large number of predictive biomarkers, i.e. mutations that impact the survival of patients who are taking specific chemo, targeted or immuno therapies (Liu et al. Nature Medicine 2022). These mutation-drug interactions are critical for treatment planning and precision oncology. Throughout the talk, I will highlight some useful computational techniques from causal inference and language models.

Bio: James Zou is an assistant professor of Biomedical Data Science, CS and EE at Stanford University. He develops data science and machine learning methods for biology and medicine. He works on both improving the foundations of AI–-by making models more trustworthy and reliable–-as well as deep scientific and clinical applications. He has received a Sloan Fellowship, an NSF CAREER Award, two Chan-Zuckerberg Investigator Awards, a Top Ten Clinical Achievement Award, several best paper awards, and faculty awards from Google, Amazon, Tencent and Adobe.”