  Cancer is a complex disease, the understanding and treatment of which are
being aided through increases in the volume of collected data and in the scale
of deployed computing power. Consequently, there is a growing need for the
development of data-driven and, in particular, deep learning methods for
various tasks such as cancer diagnosis, detection, prognosis, and prediction.
Despite recent successes, however, designing high-performing deep learning
models for nonimage and nontext cancer data is a time-consuming,
trial-and-error, manual task that requires both cancer domain and deep learning
expertise. To that end, we develop a reinforcement-learning-based neural
architecture search to automate deep-learning-based predictive model
development for a class of representative cancer data. We develop custom
building blocks that allow domain experts to incorporate the
cancer-data-specific characteristics. We show that our approach discovers deep
neural network architectures that have significantly fewer trainable
parameters, shorter training time, and accuracy similar to or higher than those
of manually designed architectures. We study and demonstrate the scalability of
our approach on up to 1,024 Intel Knights Landing nodes of the Theta
supercomputer at the Argonne Leadership Computing Facility.
