Journal:AI meets exascale computing: Advancing cancer research with large-scale high-performance computing
| Full article title | AI meets exascale computing: Advancing cancer research with large-scale high-performance computing |
|---|---|
| Journal | Frontiers in Oncology |
| Author(s) |
Bhattacharya, Tanmoy; Brettin, Thomas; Doroshow, James H.; Evrard, Yvonne A.; Greenspan, Emily J.; Gryshuk, Amy L.; Hoang, Thuc T.; Vea Lauzon, Carolyn, B.; Nissley, Dwight; Penberthy, Lynne; Stahlberg, Eric; Stevens, Rick; Streitz, Fred; Tourassi, Georgia; Xia, Fangfang; Zaki, George |
| Author affiliation(s) |
Los Alamos National Laboratory, Argonne National Laboratory, National Cancer Institute, Frederick National Laboratory for Cancer Research, Lawrence Livermore National Laboratory, National Nuclear Security Administration, U.S. Department of Energy Office of Science, University of Chicago, Oak Ridge National Laboratory |
| Primary contact | Email: george dot zaki at nih dot gov |
| Editors | Meerzaman, Daoud |
| Year published | 2019 |
| Volume and issue | 9 |
| Page(s) | 984 |
| DOI | 10.3389/fonc.2019.00984 |
| ISSN | 2234-943X |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | https://www.frontiersin.org/articles/10.3389/fonc.2019.00984/full |
| Download | https://www.frontiersin.org/articles/10.3389/fonc.2019.00984/pdf (PDF) |
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This article should be considered a work in progress and incomplete. Consider this article incomplete until this notice is removed. |
Abstract
The application of data science in cancer research has been boosted by major advances in three primary areas: (1) data: diversity, amount, and availability of biomedical data; (2) advances in artificial intelligence (AI) and machine learning (ML) algorithms that enable learning from complex, large-scale data; and (3) advances in computer architectures allowing unprecedented acceleration of simulation and machine learning algorithms. These advances help build in silico ML models that can provide transformative insights from data, including molecular dynamics simulations, next-generation sequencing, omics, imaging, and unstructured clinical text documents. Unique challenges persist, however, in building ML models related to cancer, including: (1) access, sharing, labeling, and integration of multimodal and multi-institutional data across different cancer types; (2) developing AI models for cancer research capable of scaling on next-generation high-performance computers; and (3) assessing robustness and reliability in the AI models. In this paper, we review the National Cancer Institute (NCI) -Department of Energy (DOE) collaboration, the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C), a multi-institution collaborative effort focused on advancing computing and data technologies to accelerate cancer research on the molecular, cellular, and population levels. This collaboration integrates various types of generated data, pre-exascale compute resources, and advances in ML models to increase understanding of basic cancer biology, identify promising new treatment options, predict outcomes, and, eventually, prescribe specialized treatments for patients with cancer.
Keywords: cancer research, high-performance computing, artificial intelligence, deep learning, natural language processing, multi-scale modeling, precision medicine, uncertainty quantification
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Notes
This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added.
