NCI-Department of Energy Collaborations

Since 2016, NCI and the U.S. Department of Energy (DOE) have engaged in a strategic, interagency collaboration to simultaneously accelerate advances in precision oncology and scientific computing. The NCI-DOE Collaboration is part of the Cancer Moonshot℠, dedicated to ending cancer as we know it.

The interdisciplinary projects under the NCI-DOE Collaboration are led jointly by NCI, DOE, NCI’s Frederick National Laboratory for Cancer Research (FNLCR), and DOE national laboratories, principally Argonne, Brookhaven, Lawrence Livermore, Los Alamos, and Oak Ridge.

NCI-DOE Projects

MOSSAIC: Modeling Outcomes Using Surveillance Data and Scalable Artificial Intelligence for Cancer

MOSSAIC applies natural language processing (NLP) and deep learning algorithms to population-based cancer data collected by NCI's Surveillance, Epidemiology, and End Results (SEER) program. MOSSAIC advances computational and informatics solutions to support SEER and lays the foundation for an integrative data-driven approach to modeling cancer outcomes at scale and in real time.

ADMIRRAL: AI-Driven Multiscale Investigation of the RAS/RAF Activation Lifecycle

ADMIRRAL aims to develop a mechanistic understanding of RAS-RAF-driven cancer initiation and growth. Combining machine learning (ML), high-performance computing, and experimentation, the project will delineate large-scale domain rearrangement (with molecular resolution) of the RAS-RAF complex and describe the activation of RAF kinase.

IMPROVE: Innovative Methodologies and New Data for Predictive Oncology Model Evaluation

IMPROVE develops approaches and a framework for comparing and evaluating deep learning drug response prediction models. The IMPROVE project team, together with the broader scientific community, is discovering methods to identify deep learning model attributes—and new data—that contribute to prediction performance and reproducibility to improve future models.

ATOM: Accelerating Therapeutics for Opportunities in Medicine

ATOM accelerates drug discovery through integrated AI, high performance computing, and biomedical data. ATOM employs active learning to identify and optimize new compounds to satisfy multiple pharmaceutical parameters concurrently. In addition, ATOM delivers open models, data, and software for use by the community to shorten the time to discovery and optimization of molecules, including new treatments, probe molecules, and imaging agents.

NCI-DOE Collaboration Infrastructure

CANDLE: CANcer Distributed Learning Environment

CANDLE is an open source, collaboratively developed software platform that provides deep learning methodologies for accelerating cancer research. Co-developed by DOE and FNLCR, with support from the DOE’s Exascale Computing Project, CANDLE is available on GitHub and deployed on the NIH Biowulf cluster.

MoDaC: NCI Predictive Oncology Modeling and Data Clearinghouse

MoDaC is a public portal delivering predictive oncology data sets and computational models. MoDaC allows researchers to easily access NCI-DOE collaboration computational resources, emphasizing data sets and associated models. The research community can download and use annotated data sets and models in this publicly searchable resource.

Machine Learning/AI Resources and Publications

You can see regular updates by checking the list of NCI-DOE collaboration software, models, data sets, workflows, and other resources, along with related publications.

Expanding Collaborations

Building on the NCI-DOE Collaboration, the Envisioning Computational Innovations for Cancer Challenges (ECICC) community is an incubator for new collaboration areas at the intersection of cancer, AI, and data science. Scientists from more than 50 public and private organizations at all career levels have participated in interactive ECICC events to share their ideas and expertise, develop use cases, and explore new interdisciplinary research opportunities. Notably, ideas emanate from the community itself. Events include Ideas Labs and workshops focused on advancing the development of cancer patient digital twin approaches and predictive radiation oncology.

Join the interdisciplinary community to stay abreast of news, events, and opportunities, and email your questions and comments to the ECICC Community.

For more information about any of the NCI-DOE Collaboration resources or to explore research collaborations, contact the NCI-DOE Resources Team.

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