Yu, Yiyang, Muthukumar, Shivani, Koo, Peter K (January 2024) EvoAug-TF: Extending evolution-inspired data augmentations for genomic deep learning to TensorFlow. bioRxiv. (Submitted)
Preview |
PDF
2024.01.17.575961v1.full.pdf - Submitted Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (485kB) | Preview |
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: | |
| 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: | |
| URI: | https://repository.cshl.edu/id/eprint/41422 |
Actions (login required)
 |
Administrator's edit/view item |