A brief summary of the planned workshop sessions is provided below. A detailed agenda with speakers and presentation titles will be shared in January.
DAY 1, April 3, 2023 (11 am to 4:30 pm EDT)
Session Chair: Trey Ideker, UCSD
This session will focus on expanding the field of structure prediction to incorporate multiple data modalities and layers of biological structure beyond the protein, as well as meta-learning for identifying targets for drug discovery.
Session Chair: Fabian Theis, Helmholtz Zentrum München
In this session, researchers will discuss the use of large-scale perturbation data for causal modeling, combining representation learning with perturbation approaches, and methods to extrapolate beyond existing perturbation data.
Session chair: Dana Pe’er, Memorial Sloan Kettering
This session will focus on multimodal learning in data limited contexts, including cell-cell interactions and predicting outcomes. Dealing with imbalances across multimodal data sets and foundational models will also be discussed.
DAY 2, April 4, 2023 (11 am to 3:30 pm EDT)
Session chair: Ziad Obermeyer, UC Berkeley
In this session, researchers will discuss the use of large-scale clinical research data for machine learning models. Discussion topics include the use of synthetic data, considerations of bias, generalizable models, and development of digital twins.
Session chair: Tianxi Cai, Harvard
This session will focus on real-world evidence (RWE) data modeling, including issues associated with RWE data such as electronic health record coding and unbalanced data, towards the development of clinical trials.
Discussion of the approaches and challenges identified during the workshop and opportunities for the future.