The following list, although not exhaustive, covers some of the main topics that will be discussed in the workshop.

• Learning on Graphs

  • Representing patients and molecules as graphs

  • Transfer learning and generative models

  • Modelling dynamic graphs

  • Node and link prediction on molecular graphs

  • Biological pathway mining for cancer biomarker discovery

  • Machine learning with brain graphs

  • Causal structure learning from real world data

• Deep learning in healthcare and pharmaceutical research

  • Medical Imaging

  • Natural Language Processing

  • Reinforcement learning in computer-assisted procedures

  • Audio Processing

  • Time series analysis

• Bayesian methods

  • Modelling uncertainty

  • Bayesian methods for classification and imputation

  • Bayesian networks

• General machine learning methods for healthcare and pharma

  • Machine learning for personalized healthcare

  • Multimodal machine learning (e.g. combining genomics, pathology reports, clinical data to predict survival)

  • Remote sensing for healthcare applications

  • Causal inference with observational data

  • Subgroup discovery and targeted learning

  • Longitudinal and trajectory data analysis

  • Medical decision support