EvoAug-TF: Extending evolution-inspired data augmentations for genomic deep learning to TensorFlow

Yu, Yiyang, Muthukumar, Shivani, Koo, Peter K (January 2024) EvoAug-TF: Extending evolution-inspired data augmentations for genomic deep learning to TensorFlow. bioRxiv. (Submitted)

PDF 2024.01.17.575961v1.full.pdf - Submitted Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (485kB)

Abstract

Deep neural networks (DNNs) have been widely applied to predict the molecular functions of regulatory regions in the non-coding genome. DNNs are data hungry and thus require many training examples to fit data well. However, functional genomics experiments typically generate limited amounts of data, constrained by the activity levels of the molecular function under study inside the cell. Recently, EvoAug was introduced to train a genomic DNN with evolution-inspired augmentations. EvoAug-trained DNNs have demonstrated improved generalization and interpretability with attribution analysis. However, EvoAug only supports PyTorch-based models, which limits its applications to a broad class of genomic DNNs based in TensorFlow. Here, we extend EvoAug’s functionality to TensorFlow in a new package we call EvoAug-TF. Through a systematic benchmark, we find that EvoAug-TF yields comparable performance with the original EvoAug package.

Item Type:	Paper
Subjects:	bioinformatics bioinformatics > genomics and proteomics bioinformatics > quantitative biology bioinformatics > computational biology > algorithms bioinformatics > computational biology bioinformatics > quantitative biology > quantitative genetics
CSHL Authors:	Koo, Peter K
Communities:	CSHL labs > Koo Lab
SWORD Depositor:	CSHL Elements
Depositing User:	CSHL Elements
Date:	18 January 2024
Date Deposited:	25 Jan 2024 14:07
Last Modified:	25 Jan 2024 14:07
Related URLs:	Publisher
URI:	https://repository.cshl.edu/id/eprint/41422

Actions (login required)

Administrator's edit/view item