Communications of the ACM
Argonne Goes Deep to Crack Cancer Code
Scientists at Argonne National Laboratory are advancing an exascale computing framework for a deep neural network code designed to tackle key challenges to accelerate cancer research.
Credit: David Kashatus/National Cancer Institute/University of Virginia Cancer Center
Scientists at Argonne National Laboratory are advancing an exascale computing framework for a deep neural network code called the CANcer Distributed Learning Environment (CANDLE).
CANDLE is designed to tackle key challenges to accelerate cancer research, merging novel data acquisition and analysis techniques, model formulation, and simulation to support individualized patient prognosis and treatment plans.
The first stage is the compilation and virtual approximation of all known data on cancer function, drug response, and behavior within individuals, with CANDLE "learning" to progressively manage these datasets.
CANDLE's network code will be trained to absorb many drug-screening results, after which an open source content management system will mine more than 1 billion drug combinations to identify those with the most tumor-inhibiting potential.
"We are trying to devise a means of automating the search through machine learning so that you'd start with an initial model and then automatically find models that perform better than the initial one," says Argonne's Rick Stevens.
From Argonne National Laboratory
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