Session

DescriptionThe minisymposium will offer an opportunity for experts in scientific computing and life sciences to share knowledge surrounding the challenging task of comparing machine learning models for cancer drug response prediction. The minisymposium, which will be presented by a range of cancer scientists and computer scientists, will provide an overview of cancer drug response prediction, and the computing challenges that are posed by this problem. Two presenters will cover the development of drug response models. These will be drawn from the community of model developers who produce models that are now available for comparison. Two other presenters will cover the usage of drug response models. These will be drawn from the community of stakeholders that use cancer models in broader research initiatives in cancer science and the development of treatments. They will describe how their team uses computational and data products, how they interact with developers, and what the future of drug response prediction may hold. This minisymposium is not simply about cancer prediction, as the collection of models that is emerging is a valuable asset to the machine learning community, and may be used for a range of studies in machine learning systems, performance, accuracy, and other behavior.