| Title: | Phenotypic evaluation of deep learning models for classifying germline variant pathogenicity |
| Journal: | npj Precision Oncology |
| Published: | 19 Oct 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39427061/ |
| DOI: | https://doi.org/10.1038/s41698-024-00710-x |
| URL: | http://dx.doi.org/10.1038/s41698-024-00710-x |
Publication 13413
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
Deep learning models for predicting variant pathogenicity have not been thoroughly evaluated on real-world clinical phenotypes. Here, we apply state-of-the-art pathogenicity prediction models to hereditary breast cancer gene variants in UK Biobank participants. Model predictions for missense variants in BRCA1, BRCA2 and PALB2, but not ATM and CHEK2, were associated with breast cancer risk. However, deep learning models had limited clinical utility when specifically applied to variants of uncertain significance.</p>
3 Authors
- Ryan D. Chow
- Katherine L. Nathanson
- Ravi B. Parikh
Enabling scientific discoveries that improve human health