| Title: | Machine-learning algorithms predict breast cancer patient survival from UK Biobank whole-exome sequencing data |
| Journal: | Biomarkers in Medicine |
| Published: | 15 Oct 2021 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/34651513/ |
| DOI: | https://doi.org/10.2217/bmm-2021-0280 |
| URL: | https://www.researchsquare.com/article/rs-115867/v1.pdf?c=1606961185000 |
| Citations: | 3 (1 in last 2 years) as of 8 Aug 2024 |
Publication 5351
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Abstract
Aim: We tested whether machine-learning algorithm could find biomarkers predicting overall survival in breast cancer patients using blood-based whole-exome sequencing data. Materials & methods: Whole-exome sequencing data derived from 1181 female breast cancer patients within the UK Biobank was collected. We found feature genes (n = 50) regarding total mutation burden using the long short-term memory model. Then, we developed the XGBoost survival model with selected feature genes. Results: The XGBoost survival model performed acceptably, with a concordance index of 0.75 and a scaled Brier score of 0.146 in terms of overall survival prediction. The high-mutation group exhibited inferior overall survival compared with the low-mutation group in patients ≥56 years (log-rank test, p = 0.042). Conclusion: We showed that machine-learning algorithms can be used to predict overall survival in breast cancer patients from blood-based whole-exome sequencing data.</p>
2 Authors
- Bum-Sup Jang
- In Ah Kim
Enabling scientific discoveries that improve human health