Despite increasing interest in applying AI algorithms to predict drug responses in cancer, understanding how complex tumor biology and biological factors contribute to these predictions remains a challenge. Researchers are exploring strategies that utilize known molecular pathways and integrate genomics, proteomics, and clinical data to build more structured and interpretable models. In this webinar, Trey Ideker will discuss developing AI models that accurately predict drug responses in human cancer cells and reveal key molecular pathways mediating these responses, offering valuable insights for more effective drug design.
Topics to be covered
- Constructing an interpretable deep learning model for predicting drug responses
- Developing neural network predictive models for various tissue types and clinical settings
Tuesday, March 26th, 2024 | 1:00 PM - 2:00 PM Eastern Time
This webinar will be available to view live and on demand.
Trey Ideker, PhD
University of California San Diego