Deep Motif: Visualizing Genomic Sequence Classifications

Jack Lanchantin, Ritambhara Singh, Zeming Lin, & Yanjun Qi

Abstract:

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.

Conflicts: cs.virginia.edu