Koo, Peter K, Ploenzke, Matt (March 2021) Improving representations of genomic sequence motifs in convolutional networks with exponential activations. Nature Machine Intelligence, 3 (3). pp. 258-266. ISSN 2522-5839
Abstract
Deep convolutional neural networks (CNNs) trained on regulatory genomic sequences tend to build representations in a distributed manner, making it a challenge to extract learned features that are biologically meaningful, such as sequence motifs. Here we perform a comprehensive analysis of synthetic sequences to investigate the role that CNN activations have on model interpretability. We show that employing an exponential activation in the first layer filters consistently leads to interpretable and robust representations of motifs compared with other commonly used activations. Strikingly, we demonstrate that CNNs with better test performance do not necessarily imply more interpretable representations with attribution methods. We find that CNNs with exponential activations significantly improve the efficacy of recovering biologically meaningful representations with attribution methods. We demonstrate that these results generalize to real DNA sequences across several in vivo datasets. Together, this work demonstrates how a small modification to existing CNNs (that is, setting exponential activations in the first layer) can substantially improve the robustness and interpretabilty of learned representations directly in convolutional filters and indirectly with attribution methods.
| Item Type: | Paper |
|---|---|
| Subjects: | bioinformatics bioinformatics > computational biology > algorithms bioinformatics > computational biology bioinformatics > genomics and proteomics > genetics & nucleic acid processing > genomes bioinformatics > computational biology > algorithms > machine learning |
| CSHL Authors: | |
| Communities: | CSHL labs > Koo Lab CSHL Cancer Center Program CSHL Cancer Center Program > Gene Regulation and Inheritance Program |
| SWORD Depositor: | CSHL Elements |
| Depositing User: | CSHL Elements |
| Date: | March 2021 |
| Date Deposited: | 07 May 2021 20:10 |
| Last Modified: | 13 Feb 2024 18:39 |
| PMCID: | PMC8315445 |
| Related URLs: | |
| URI: | https://repository.cshl.edu/id/eprint/40065 |
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