Lee, Nicholas Keone, Tang, Ziqi, Toneyan, Shushan, Koo, Peter (2022) EvoAug: improving generalization and interpretability of genomic deep neural networks with evolution-inspired data augmentations. bioRxiv. (Submitted)
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Abstract
Deep neural networks (DNNs) hold promise for functional genomics prediction, but their generalization capability may be limited by the amount of available data. To address this, we propose EvoAug, a suite of evolution-inspired augmentations that enhance the training of genomic DNNs by increasing genetic variation. However, random transformation of DNA sequences can potentially alter their function in unknown ways. Thus, we employ a fine-tuning procedure using the original non-transformed data to preserve functional integrity. Our results demonstrate that EvoAug substantially improves the generalization and interpretability of established DNNs across prominent regulatory genomics prediction tasks, offering a robust solution for genomic DNNs.
| Item Type: | Paper |
|---|---|
| Subjects: | bioinformatics > genomics and proteomics > genetics & nucleic acid processing > genomes organs, tissues, organelles, cell types and functions > tissues types and functions > neural networks |
| CSHL Authors: | |
| Communities: | CSHL labs > Koo Lab School of Biological Sciences > Publications |
| SWORD Depositor: | CSHL Elements |
| Depositing User: | CSHL Elements |
| Date: | 2022 |
| Date Deposited: | 02 Oct 2023 17:36 |
| Last Modified: | 29 Feb 2024 18:13 |
| PMCID: | PMC10161416 |
| Related URLs: | |
| URI: | https://repository.cshl.edu/id/eprint/41095 |
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