  Each human genome is a 3 billion base pair set of encoding instructions.
Decoding the genome using deep learning fundamentally differs from most tasks,
as we do not know the full structure of the data and therefore cannot design
architectures to suit it. As such, architectures that fit the structure of
genomics should be learned not prescribed. Here, we develop a novel search
algorithm, applicable across domains, that discovers an optimal architecture
which simultaneously learns general genomic patterns and identifies the most
important sequence motifs in predicting functional genomic outcomes. The
architectures we find using this algorithm succeed at using only RNA expression
data to predict gene regulatory structure, learn human-interpretable
visualizations of key sequence motifs, and surpass state-of-the-art results on
benchmark genomics challenges.
