ABSTRACT
Data augmentations can greatly enhance the generalization of deep neural networks (DNNs). However, there are limited strategies available in regulatory genomics because modifying DNA can alter its function in unknown ways. Here we introduce a suite of evolution-inspired augmentations and circumvent issues of unknown function through a fine-tuning procedure using the original, unperturbed data. We demonstrate this approach improves generalization and model interpretability across several established DNNs in regulatory genomics.
Competing Interest Statement
The authors have declared no competing interest.
Copyright
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.
Posted November 04, 2022.
