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Interpreting Deep Neural Networks Beyond Attribution Methods: Quantifying Global Importance of Genomic Features

Peter K. Koo, Matt Ploenzke
doi: https://doi.org/10.1101/2020.02.19.956896
Peter K. Koo
1Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory
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  • For correspondence: koo@cshl.edu
Matt Ploenzke
2Department of Biostatistics, Harvard University
3Department of Data Sciences, Dana-Farber Cancer Institute
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Abstract

Despite deep neural networks (DNNs) having found great success at improving performance on various prediction tasks in computational genomics, it remains difficult to understand why they make any given prediction. In genomics, the main approaches to interpret a high-performing DNN are to visualize learned representations via weight visualizations and attribution methods. While these methods can be informative, each has strong limitations. For instance, attribution methods only uncover the independent contribution of single nucleotide variants in a given sequence. Here we discuss and argue for global importance analysis which can quantify population-level importance of putative features and their interactions learned by a DNN. We highlight recent work that has benefited from this interpretability approach and then discuss connections between global importance analysis and causality.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted February 20, 2020.
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Interpreting Deep Neural Networks Beyond Attribution Methods: Quantifying Global Importance of Genomic Features
Peter K. Koo, Matt Ploenzke
bioRxiv 2020.02.19.956896; doi: https://doi.org/10.1101/2020.02.19.956896
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Interpreting Deep Neural Networks Beyond Attribution Methods: Quantifying Global Importance of Genomic Features
Peter K. Koo, Matt Ploenzke
bioRxiv 2020.02.19.956896; doi: https://doi.org/10.1101/2020.02.19.956896

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