Date: October 24, 2023
Venue: In-Person & Zoom
Speaker: Takis Benos, Ph.D., William Bushnell Presidential Chair & Professor
Affiliation: Department of Epidemiology, University of Florida
Title: Interpretable machine learning models for medical research
Abstract: The advancement of technologies for high-throughput collection of molecular and clinical 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. My group has historically focused on developing statistical and computational methods to identify key molecules (genes, microRNAs, etc) that affect biological processes, disease onset and progression. We are also interested in how we can combine the power of genomics with the rich multi-scale data that are available in biology and medicine. In this talk, I will present two aspects of our work: (1) how interpretable machine learning (causal) models can advance biomedical research, and (2) our recent findings on the use of such methods in addressing important questions in lung diseases.