  This paper applies a deep convolutional/highway MLP framework to classify
genomic sequences on the transcription factor binding site task. To make the
model understandable, we propose an optimization driven strategy to extract
"motifs", or symbolic patterns which visualize the positive class learned by
the network. We show that our system, Deep Motif (DeMo), extracts motifs that
are similar to, and in some cases outperform the current well known motifs. In
addition, we find that a deeper model consisting of multiple convolutional and
highway layers can outperform a single convolutional and fully connected layer
in the previous state-of-the-art.

Machine Learning