BRCAGenie: A machine learning-driven 43-gene polygenic risk score model for precision prediction of breast cancer survival
The development of an accurate breast cancer polygenic risk score model for survival prediction enables robust prognostic stratification for personalised clinical decision-making. Through our unbiased, hypothesis-free approach, we have identified novel breast cancer prognostic biomarkers with the potential to be clinically validated and translated into relevant diagnostics and therapeutics.
Authors
- Lee Jin Lim (ORCID: https://orcid.org/0000-0002-9563-9063)
- Jun Hao Neo
- Ashley J. W. Lim (ORCID: https://orcid.org/0000-0002-5641-075X)
- John Lee (ORCID: https://orcid.org/0000-0002-0914-2975)
- Caroline Guat Lay Lee (ORCID: https://orcid.org/0000-0002-4323-3635)
- Chengyi Wang (ORCID: https://orcid.org/0000-0002-6780-9299)
Institutions
- National University of Singapore (SG)
- Duke-NUS Medical School (SG)
Publication Details
- Journal
- Journal of Translational Medicine
- Published
- 2025-10-29
- DOI
- https://doi.org/10.1186/s12967-025-07100-2
- Primary Topic
- BRCA gene mutations in cancer
- Type
- article
- Field-Weighted Citation Impact
- 0.00