  When analyzing the genome, researchers have discovered that proteins bind to
DNA based on certain patterns of the DNA sequence known as "motifs". However,
it is difficult to manually construct motifs due to their complexity. Recently,
externally learned memory models have proven to be effective methods for
reasoning over inputs and supporting sets. In this work, we present memory
matching networks (MMN) for classifying DNA sequences as protein binding sites.
Our model learns a memory bank of encoded motifs, which are dynamic memory
modules, and then matches a new test sequence to each of the motifs to classify
the sequence as a binding or nonbinding site.
