In this video, Jonathan Allen from LLNL describes how Lawrence Livermore’s supercomputers are playing a crucial role in advancing cancer research and treatment. “A historic partnership between the Department of Energy (DOE) and the National Cancer Institute (NCI) is applying the formidable computing resources at Livermore and other DOE national laboratories to advance cancer research and treatment. Announced in late 2015, the effort will help researchers and physicians better understand the complexity of cancer, choose the best treatment options for every patient, and reveal possible patterns hidden in vast patient and experimental data sets.”
Supercomputing the Cancer Moonshot and Beyond
October 22, 2016 by
In this video, Dr. Dimitri Kusnezov from the U.S. Department of Energy National Nuclear Security Administration presents: Supercomputing the Cancer Moonshot and Beyond. “How can the next generation of supercomputers unlock biomedical mysteries that will shape the future practice of medicine? Scientists behind the National Strategic Computing Initiative, a federal strategy for investing in high-performance computing, are exploring this question.”
ORNL Supercomputers to Boost National Cancer Moonshot
July 29, 2016 by
The Department of Energy’s Oak Ridge National Laboratory will add its computational know-how to the battle against cancer through several new projects recently announced at the White House Cancer Moonshot Summit. “ORNL brings to the table our world-class resources in high-performance computing, including the Titan supercomputer, as well as leading experts in the data sciences and neutron analysis, to the fight against cancer,” said ORNL’s Joe Lake, deputy for operations at the HDSI.”
Supercomputers Joining the Fight Against Cancer
June 17, 2016 by
“Supercomputers are key to the Cancer Moonshot. These exceptionally high-powered machines have the potential to greatly accelerate the development of cancer therapies by finding patterns in massive datasets too large for human analysis. Supercomputers can help us better understand the complexity of cancer development, identify novel and effective treatments, and help elucidate patterns in vast and complex data sets that advance our understanding of cancer.”
