Evaluating deep learning for predicting epigenomic profiles

Toneyan, Shushan, Tang, Ziqi, Koo, Peter K (May 2022) Evaluating deep learning for predicting epigenomic profiles. BioRxiv. (Unpublished)

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URL: https://www.biorxiv.org/content/10.1101/2022.04.29...
DOI: 10.1101/2022.04.29.490059


Deep learning has been successful at predicting epigenomic profiles from DNA sequences. Most approaches frame this task as a binary classification relying on peak callers to define functional activity. Recently, quantitative models have emerged to directly predict the experimental coverage values as a regression. As new models continue to emerge with different architectures and training configurations, a major bottleneck is forming due to the lack of ability to fairly assess the novelty of proposed models and their utility for downstream biological discovery. Here we introduce a unified evaluation framework and use it to compare various binary and quantitative models trained to predict chromatin accessibility data. We highlight various modeling choices that affect generalization performance, including a downstream application of predicting variant effects. In addition, we introduce a robustness metric that can be used to enhance model selection and improve variant effect predictions. Our empirical study largely supports that quantitative modeling of epigenomic profiles leads to better generalizability and interpretability.

Item Type: Paper
Subjects: bioinformatics > genomics and proteomics
bioinformatics > computational biology
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > epigenetics
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification > epigenetics
CSHL Authors:
Communities: CSHL labs > Koo Lab
SWORD Depositor: CSHL Elements
Depositing User: CSHL Elements
Date: 1 May 2022
Date Deposited: 18 May 2022 20:38
Last Modified: 18 May 2022 20:38
URI: https://repository.cshl.edu/id/eprint/40615

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