Fraction genome altered outperforms tumor mutational burden for machine learning-based mortality prediction across TP53, PIK3CA, GATA3, and MAP3K1 subtypes in invasive ductal carcinoma
- DOI
- 10.1007/s10354-026-01202-2
- Published
- 2026-11-06
- Container
- Wiener Medizinische Wochenschrift
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1007/s10354-026-01202-2,
title = {Fraction genome altered outperforms tumor mutational burden for machine learning-based mortality prediction across TP53, PIK3CA, GATA3, and MAP3K1 subtypes in invasive ductal carcinoma},
author = {Bandana Kumari and Rijhul Lahariya and Gargee Anand},
year = {2026},
journal = {Wiener Medizinische Wochenschrift},
doi = {10.1007/s10354-026-01202-2},
url = {https://doi.org/10.1007/s10354-026-01202-2}
}RIS
TY - JOUR TI - Fraction genome altered outperforms tumor mutational burden for machine learning-based mortality prediction across TP53, PIK3CA, GATA3, and MAP3K1 subtypes in invasive ductal carcinoma AU - Bandana Kumari AU - Rijhul Lahariya AU - Gargee Anand PY - 2026 JO - Wiener Medizinische Wochenschrift DO - 10.1007/s10354-026-01202-2 UR - https://doi.org/10.1007/s10354-026-01202-2 ER -
APA
Kumari, B., Lahariya, R., & Anand, G. (2026). Fraction genome altered outperforms tumor mutational burden for machine learning-based mortality prediction across TP53, PIK3CA, GATA3, and MAP3K1 subtypes in invasive ductal carcinoma. Wiener Medizinische Wochenschrift. https://doi.org/10.1007/s10354-026-01202-2
Source records
- crossref · retrieved 2026-11-09T09:33:15.820Z