In this talk we will describe our recently funded effort to create the CANcer Distributed Learning Environment (CANDLE toolkit) to address one of the challenges i"> In this talk we will describe our recently funded effort to create the CANcer Distributed Learning Environment (CANDLE toolkit) to address one of the challenges i" /> In this talk we will describe our recently funded effort to create the CANcer Distributed Learning Environment (CANDLE toolkit) to address one of the challenges i"> CANDLE the CANcer Distributed Learning Environment -- Scalable Deep Learning for Precision Medicine | Supercomputing 2016
 

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GTC ON-DEMAND

HPC and Supercomputing
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CANDLE the CANcer Distributed Learning Environment -- Scalable Deep Learning for Precision Medicine
Abstract:

In this talk we will describe our recently funded effort to create the CANcer Distributed Learning Environment (CANDLE toolkit) to address one of the challenges identified in the presidential “Precision Medicine Initiative" (PMI). The DOE laboratories in this project are drawing on their strengths in HPC, machine learning and data analytics, and coupling those to the domain strengths of the NCI, particularly in cancer biology and cancer healthcare delivery to bring the full promise of exascale computing to the problem of cancer and precision medicine. This project will focus on three driver cancer problems: RAS protein pathway, drug response, and treatment strategies. We will provide a highlight of these problems, as well as a roadmap for the projects intended research and development efforts.

 
Topics:
HPC and Supercomputing, Artificial Intelligence and Deep Learning
Type:
Talk
Event:
Supercomputing
Year:
2016
Session ID:
SC6124
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