EvoAug: improving generalization and interpretability of genomic deep neural networks with evolution-inspired data augmentations

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:	Tang, Ziqi Toneyan, Shushan Koo, Peter K Lee, Nicholas
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:	Publisher
URI:	https://repository.cshl.edu/id/eprint/41095

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