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Improving representations of genomic sequence motifs in convolutional networks with exponential activations

A preprint version of the article is available at bioRxiv.

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.

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Fig. 1: Motif representation performance.
Fig. 2: Interpretability performance of saliency maps.
Fig. 3: Attribution score comparison for real regulatory DNA sequences.

Data availability

Data for tasks 1, 3, 5 and 6, and code to generate data for Task 2, are available at https://doi.org/10.5281/zenodo.4301062. Data for Task 4 are available via ref. 1.

Code availability

Code to reproduce results and figures is available at https://doi.org/10.5281/zenodo.4301062.

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Acknowledgements

This work was supported in part by funding from the NCI Cancer Center Support Grant (CA045508) and the Simons Center for Quantitative Biology at Cold Spring Harbor Laboratory. M.P. was supported by NIH NCI RFA-CA-19-002. The authors would like to thank D. Krotov, who provided inspiration for the exponential activation. We would also like to thank J. Kinney, A. Tareen and the members of the Koo laboratory for helpful discussions.

Author information

Authors and Affiliations

Authors

Contributions

P.K.K. conceived of the experiments. P.K.K. and M.P. conducted the experiments. P.K.K. and M.P. analysed the results. All authors reviewed the manuscript.

Corresponding author

Correspondence to Peter K. Koo.

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The authors declare no competing interests.

Additional information

Peer review information Nature Machine Intelligence thanks Smita Krishnaswamy and the other, anonymous reviewer(s), for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Task 1 motif representations for CNNs with modified activations.

a, Boxplot of the fraction of filters that match ground truth motifs for different CNNs with traditional and modified activations. b, Boxplot of the fraction of filters that match ground truth motifs for an ablation study of transformations for modified activations. c, First layer filter scans from CNN-deep with relu activations (top) and exponential activations (middle). Each colour represents a different filter. d, Motif scans (top) and PWM scans (middle) using ground truth motifs and their reverse-complements (each colour represents a different filter scan). Negative PWM scan values were rectified to a value of zero. c,d, The information content of the sequence model used to generate the synthetic sequence (ground truth), which has 3 embedded motifs centred at positions 15, 85, and 150, is shown at the bottom. e, Boxplot of the fraction of filters that match ground truth motifs for CNN-deep with various activations: log activations trained with and without L2-regularization (Log-Relu-L2 and Log-Relu, respectively) and relu activations with and without L2-regularization. a,b,e, Each boxplot represents the performance across 10 models trained with different random intialisations (box represents first and third quartile and the red line represents the median).

Extended Data Fig. 2 Interpretability performance comparison of different attribution methods.

Boxplots of the interpretability AUROC (a) and AUPR b, for CNN-local (top) and CNN-dist (bottom) with relu activations (left) and exponential activations (right) for different attribution methods. Each boxplot represents the performance across 10 models trained with different random intialisations (box represents first and third quartile and the red line represents the median). Sequence logo of a saliency map for a Task 3 test sequence generated with different attribution methods for CNN-deep with relu activations (c) and exponential activations (d). The right y-axis label shows the interpretability AUROC score. c-d, The sequence logo for the ground truth sequence model is shown at the bottom.

Supplementary information

Supplementary Information

Supplementary Tables 1–8 and Figs. 1–16.

Reporting Summary

Supplementary Data

TomTom results for Task 2 and Task 4.

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Koo, P.K., Ploenzke, M. Improving representations of genomic sequence motifs in convolutional networks with exponential activations. Nat Mach Intell 3, 258–266 (2021). https://doi.org/10.1038/s42256-020-00291-x

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