Towards More Realistic Simulated Datasets for Benchmarking Deep Learning Models in Regulatory Genomics Article Swipe
Eva Prakash
,
Avanti Shrikumar
,
Anshul Kundaje
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1101/2021.12.26.474224
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1101/2021.12.26.474224
Deep neural networks and support vector machines have been shown to accurately predict genome-wide signals of regulatory activity from raw DNA sequences. These models are appealing in part because they can learn predictive DNA sequence features without prior assumptions. Several methods such as in-silico mutagenesis, GradCAM, DeepLIFT, Integrated Gradients and Gkm-Explain have been developed to reveal these learned features. However, the behavior of these methods on regulatory genomic data remains an area of active research. Although prior work has benchmarked these methods on simulated datasets with known ground-truth motifs, these simulations employed highly simplified regulatory logic that is not representative of the genome. In this work, we propose a novel pipeline for designing simulated data that comes closer to modeling the complexity of regulatory genomic DNA. We apply the pipeline to build simulated datasets based on publicly-available chromatin accessibility experiments and use these datasets to bench-mark different interpretation methods based on their ability to identify ground-truth motifs. We find that a GradCAM-based method, which was reported to perform well on a more simplified dataset, does not do well on this dataset (particularly when using an architecture with shorter convolutional kernels in the first layer), and we theoretically show that this is expected based on the nature of regulatory genomic data. We also show that Integrated Gradients sometimes performs worse than gradient-times-input, likely owing to its linear interpolation path. We additionally explore the impact of user-defined settings on the interpretation methods, such as the choice of “reference”/”baseline”, and identify recommended settings for genomics. Our analysis suggests several promising directions for future research on these model interpretation methods. Code and links to data are available at https://github.com/kundajelab/interpret-benchmark .
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2021.12.26.474224
- https://www.biorxiv.org/content/biorxiv/early/2021/12/27/2021.12.26.474224.full.pdf
- OA Status
- green
- Cited By
- 12
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4200413006
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4200413006Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2021.12.26.474224Digital Object Identifier
- Title
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Towards More Realistic Simulated Datasets for Benchmarking Deep Learning Models in Regulatory GenomicsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
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2021-12-27Full publication date if available
- Authors
-
Eva Prakash, Avanti Shrikumar, Anshul KundajeList of authors in order
- Landing page
-
https://doi.org/10.1101/2021.12.26.474224Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2021/12/27/2021.12.26.474224.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.biorxiv.org/content/biorxiv/early/2021/12/27/2021.12.26.474224.full.pdfDirect OA link when available
- Concepts
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Computer science, Pipeline (software), Artificial intelligence, Benchmarking, Convolutional neural network, Deep learning, Machine learning, Genomics, Ground truth, Data mining, Computational biology, Genome, Biology, Gene, Business, Programming language, Marketing, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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12Total citation count in OpenAlex
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2025: 2, 2024: 1, 2023: 6, 2022: 2, 2021: 1Per-year citation counts (last 5 years)
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32Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| corresponding_author_ids | https://openalex.org/A5053034507, https://openalex.org/A5083952534 |
| countries_distinct_count | 1 |
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| corresponding_institution_ids | https://openalex.org/I97018004 |
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| citation_normalized_percentile.value | 0.73536625 |
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| citation_normalized_percentile.is_in_top_10_percent | False |