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Title: AI Meets Exascale Computing: Advancing Cancer Research With Large-Scale High Performance Computing

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, 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 three levels: molecular, cellular, and population. This collaboration integrates various types of generated data, pre-exascale compute resources, and advancesmore » 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.« less

Authors:
 [1];  [2];  [3];  [4];  [3];  [5];  [6];  [7];  [4];  [3];  [4];  [8];  [5];  [9];  [2];  [4]
+ Show Author Affiliations
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Argonne National Lab. (ANL), Lemont, IL (United States)
  3. National Cancer Inst., Bethesda, MD (United States)
  4. Frederick National Lab. of Cancer Research, Frederick, MD (United States)
  5. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  6. National Nuclear Security Administration (NNSA), Washington, DC (United States)
  7. Dept. of Energy (DOE), Washington DC (United States)
  8. Argonne National Lab. (ANL), Argonne, IL (United States); Univ. of Chicago, IL (United States)
  9. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Publication Date:
Research Org.:
Argonne National Laboratory (ANL), Argonne, IL (United States); Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA); National Institutes of Health (NIH) - National Cancer Institute; USDOE Exascale Computing Project; USDOE Office of Science (SC)
OSTI Identifier:
1637271
Alternate Identifier(s):
OSTI ID: 1657127; OSTI ID: 1821812
Report Number(s):
LA-UR-19-24131; LLNL-JRNL-773355
Journal ID: ISSN 2234-943X; 159254
Grant/Contract Number:  
AC02-06CH11357; AC52-07NA27344; 89233218CNA000001
Resource Type:
Accepted Manuscript
Journal Name:
Frontiers in Oncology
Additional Journal Information:
Journal Volume: 9; Journal ID: ISSN 2234-943X
Publisher:
Frontiers Research Foundation
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 60 APPLIED LIFE SCIENCES; artificial intelligence; cancer research; deep learning; high performance computing; multi-scale modeling; natural language processing; precision medicine; uncertainty quantification

Citation Formats

Bhattacharya, Tanmoy, Brettin, Thomas, Doroshow, James H., Evrard, Yvonne A., Greenspan, Emily J., Gryshuk, Amy L., Hoang, Thuc T., Lauzon, Carolyn B. Vea, Nissley, Dwight, Penberthy, Lynne, Stahlberg, Eric, Stevens, Rick, Streitz, Fred, Tourassi, Georgia, Xia, Fangfang, and Zaki, George. AI Meets Exascale Computing: Advancing Cancer Research With Large-Scale High Performance Computing. United States: N. p., 2019. Web. doi:10.3389/fonc.2019.00984.
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Bhattacharya, Tanmoy, Brettin, Thomas, Doroshow, James H., Evrard, Yvonne A., Greenspan, Emily J., Gryshuk, Amy L., Hoang, Thuc T., Lauzon, Carolyn B. Vea, Nissley, Dwight, Penberthy, Lynne, Stahlberg, Eric, Stevens, Rick, Streitz, Fred, Tourassi, Georgia, Xia, Fangfang, & Zaki, George. AI Meets Exascale Computing: Advancing Cancer Research With Large-Scale High Performance Computing. United States. https://doi.org/10.3389/fonc.2019.00984
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Bhattacharya, Tanmoy, Brettin, Thomas, Doroshow, James H., Evrard, Yvonne A., Greenspan, Emily J., Gryshuk, Amy L., Hoang, Thuc T., Lauzon, Carolyn B. Vea, Nissley, Dwight, Penberthy, Lynne, Stahlberg, Eric, Stevens, Rick, Streitz, Fred, Tourassi, Georgia, Xia, Fangfang, and Zaki, George. Wed . "AI Meets Exascale Computing: Advancing Cancer Research With Large-Scale High Performance Computing". United States. https://doi.org/10.3389/fonc.2019.00984. https://www.osti.gov/servlets/purl/1637271.
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@article{osti_1637271,
title = {AI Meets Exascale Computing: Advancing Cancer Research With Large-Scale High Performance Computing},
author = {Bhattacharya, Tanmoy and Brettin, Thomas and Doroshow, James H. and Evrard, Yvonne A. and Greenspan, Emily J. and Gryshuk, Amy L. and Hoang, Thuc T. and Lauzon, Carolyn B. Vea and Nissley, Dwight and Penberthy, Lynne and Stahlberg, Eric and Stevens, Rick and Streitz, Fred and Tourassi, Georgia and Xia, Fangfang and Zaki, George},
abstractNote = {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, 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 three levels: molecular, cellular, and population. 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.},
doi = {10.3389/fonc.2019.00984},
journal = {Frontiers in Oncology},
number = ,
volume = 9,
place = {United States},
year = {Wed Oct 02 00:00:00 EDT 2019},
month = {Wed Oct 02 00:00:00 EDT 2019}
}
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Journal Article:
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Publisher's Version of Record
https://doi.org/10.3389/fonc.2019.00984
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Cited by: 9 works
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FIGURE 1 FIGURE 1: Pilot 1 research aims, general workflow, and supporting data.
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