Deep Motif: Visualizing Genomic Sequence Classifications

2016/05/04 by Jack Lanchantin, Lanchantin, Jack, Ritambhara Singh +5
Biochemistry, Genetics and Molecular Biology · #FOS: Computer and information sciences #Genetic and phenotypic traits in livestock #Genomics and Chromatin Dynamics #Genomics and Phylogenetic Studies #Machine Learning (cs.LG)

paper · pdf · doi:10.48550/arxiv.1605.01133

openalex publication_date 2016/05/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Understanding Neural Networks Through Deep Visualization
• Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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