Modeling Complex Phenotypes by Sayan Mukherjee

Explore the intricacies of modeling complex phenotypes in this lecture by Sayan Mukherjee, part of the "Machine Learning for Health and Disease" program at the International Centre for Theoretical Sciences. Delve into advanced techniques for analyzing and predicting health-related outcomes using machine learning approaches. Learn how to bridge the gap between computational modeling and clinical applications, with a focus on integrating diverse data types to understand complex biological traits. Gain insights into the challenges and opportunities of applying machine learning to healthcare, including predictive modeling for patient outcomes, analysis of medical imaging data, and genomic variant interpretation. Suitable for PhD students in STEM fields, medical students, postdoctoral fellows, faculty, and professionals in science, engineering, and medicine seeking to enhance their understanding of machine learning applications in biomedicine and public health.