Majdandzic, Antonio, Rajesh, Chandana, Koo, Peter K (May 2023) Correcting gradient-based interpretations of deep neural networks for genomics. Genome Biology, 24 (1). p. 109. ISSN 1474-760X
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Abstract
Post hoc attribution methods can provide insights into the learned patterns from deep neural networks (DNNs) trained on high-throughput functional genomics data. However, in practice, their resultant attribution maps can be challenging to interpret due to spurious importance scores for seemingly arbitrary nucleotides. Here, we identify a previously overlooked attribution noise source that arises from how DNNs handle one-hot encoded DNA. We demonstrate this noise is pervasive across various genomic DNNs and introduce a statistical correction that effectively reduces it, leading to more reliable attribution maps. Our approach represents a promising step towards gaining meaningful insights from DNNs in regulatory genomics.
| Item Type: | Paper |
|---|---|
| Subjects: | bioinformatics bioinformatics > genomics and proteomics organs, tissues, organelles, cell types and functions > tissues types and functions > neural networks organs, tissues, organelles, cell types and functions organs, tissues, organelles, cell types and functions > tissues types and functions |
| 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: | 9 May 2023 |
| Date Deposited: | 28 Sep 2023 19:25 |
| Last Modified: | 09 Feb 2024 15:28 |
| PMCID: | PMC10169356 |
| Related URLs: | |
| URI: | https://repository.cshl.edu/id/eprint/41046 |
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