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

Yu, Yiyang, Muthukumar, Shivani, Koo, Peter K (February 2024) EvoAug-TF: Extending evolution-inspired data augmentations for genomic deep learning to TensorFlow. Bioinformatics. ISSN 1367-4811

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Abstract

SUMMARY: Deep neural networks (DNNs) have been widely applied to predict the molecular functions of 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. AVAILABILITY: EvoAug-TF is freely available for users and is distributed under an open-source MIT license. Researchers can access the open-source code on GitHub (https://github.com/p-koo/evoaug-tf). The pre-compiled package is provided via PyPI (https://pypi.org/project/evoaug-tf) with in-depth documentation on ReadTheDocs (https://evoaug-tf.readthedocs.io). The scripts for reproducing the results are available at (https://github.com/p-koo/evoaug-tf_analysis).

Item Type:	Paper
Subjects:	bioinformatics bioinformatics > genomics and proteomics bioinformatics > computational biology > algorithms bioinformatics > computational biology bioinformatics > computational biology > algorithms > machine learning
CSHL Authors:	Koo, Peter K Yu, Yiyang Muthukumar, Shivani
Communities:	CSHL labs > Koo Lab
SWORD Depositor:	CSHL Elements
Depositing User:	CSHL Elements
Date:	16 February 2024
Date Deposited:	23 Feb 2024 13:47
Last Modified:	23 Feb 2024 13:47
Related URLs:	Publisher
URI:	https://repository.cshl.edu/id/eprint/41440

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