NCI Biomedical Informatics Blog
- Shape the Data Sharing Landscape: Make a Difference
- NCI’s Office of Data Sharing: Setting a “Gold” Standard for Childhood Cancer
- The Promise and the Challenge of Deep Learning in Pathology
- Predictive Modeling for Pre-clinical Drug Screening: Improving Models Derived From Observational Studies Using Machine Learning and Simulation
Project CANDLE — Exascale CANcer Distributed Learning Environment
Cancer is an extremely complex disease, which disrupts basic biological processes at a fundamental level leading to renegade cells threatening the health of the individual. Fortunately, with major technological advances in molecular sequencing, molecular and cellular imaging, and high-throughput screening techniques, we are now able to probe the complexity of the disease at an unparalleled level, which provides a window into the behavior of the disease at unprecedented time and spatial scales.
Using the challenges within each JDACS4C pilot to shape priorities for the CANDLE project, the DOE laboratories are drawing upon their strengths in high-performance computing (HPC), machine learning and data analytics, coupled with domain strengths at NCI and Frederick National Laboratory for Cancer Research to establish the foundations for CANDLE. Exploiting exascale technologies and capabilities anticipated for deep and machine learning, the project is scheduled to deliver critical technologies to the community that will be used to advance precision oncology. Learn more about Argonne National Laboratory’s approach to developing CANDLE. Argonne is one of the four involved DOE National Laboratories, along with Lawrence Livermore National Laboratory, Los Alamos National Laboratory, and Oak Ridge National Laboratory.
In the context of the National Strategic Computing Initiative and Cancer Moonshot, CANDLE will enable and greatly accelerate the capabilities needed to realize the promise envisioned for the Cancer Moonshot. In addition, it will establish a new paradigm for cancer research for years to come by making effective use of the ever-growing volumes and diversity of cancer-related data to build predictive models, provide better understanding of the disease and, ultimately, provide guidance and support decisions on anticipated outcomes of treatment for individual patients.