Toneyan, Shushan, Tang, Ziqi, Koo, Peter K (December 2022) Evaluating deep learning for predicting epigenomic profiles. Nature Machine Intelligence, 4 (12). pp. 1088-1100. ISSN 2522-5839
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Abstract
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 bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification bioinformatics > genomics and proteomics > genetics & nucleic acid processing bioinformatics > genomics and proteomics bioinformatics > computational biology > algorithms 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 bioinformatics > computational biology > algorithms > machine learning |
| CSHL Authors: | |
| Communities: | CSHL labs > Koo Lab School of Biological Sciences > Publications |
| SWORD Depositor: | CSHL Elements |
| Depositing User: | CSHL Elements |
| Date: | December 2022 |
| Date Deposited: | 02 Oct 2023 18:17 |
| Last Modified: | 29 Feb 2024 18:12 |
| PMCID: | PMC10270674 |
| Related URLs: | |
| URI: | https://repository.cshl.edu/id/eprint/41100 |
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