Deep Motif: Visualizing Genomic Sequence Classifications
This work addresses the need for interpretable genomic sequence classification for researchers in bioinformatics, though it appears incremental as it builds on existing deep learning methods.
The paper tackles the problem of classifying genomic sequences for transcription factor binding sites using a deep convolutional/highway MLP framework, and it results in a system called Deep Motif (DeMo) that extracts motifs similar to or outperforming current ones while showing that deeper models outperform previous state-of-the-art.
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.