Argonne researchers have created a neural architecture search that automates the development of deep-learning-based predictive models for cancer data. While increasing swaths of collected data and growing scales of computing power are helping to improve our understanding of cancer, further development of data-driven methods for the disease's diagnosis, detection and prognosis is necessary. There is a particular need to develop deep-learning methods -- that is, machine learning algorithms capable of extracting science from unstructured data. Researchers from the U.S. Department of Energy's (DOE) Argonne National Laboratory have made strides toward accelerating such efforts by presenting a method for the automated generation of neural networks. As detailed in a paper for presentation at the SC19 conference, the researchers, utilizing resources from the Argonne Leadership Computing Facility (ALCF), a DOE Office of Science User Facility, have established a neural architecture search (NAS) that, for a class of representative cancer data, automates the development of deep-learning-based predictive models.