Optimizing breast cancer therapy: chemoressitance and machine learning for precision prediction
The chemoresistance platform identified resistance patterns highlighting its utility in precision medicine. Additionally, the XGBoost algorithm accurately predicted NACT response, supporting the integration of AI with functional precision medicine for personalized BC treatment.
Authors
- Isabela Miranda (ORCID: https://orcid.org/0000-0002-9878-8996)
- Matheus Dalmolin (ORCID: https://orcid.org/0000-0002-5400-0909)
- Antônio Luiz Frasson (ORCID: https://orcid.org/0000-0003-1860-6898)
- Bianca Soares da Silva Marques (ORCID: https://orcid.org/0000-0002-4316-2152)
- Camila Alves Silva (ORCID: https://orcid.org/0000-0002-4243-7767)
- Júlia Caroline Marcolin (ORCID: https://orcid.org/0000-0002-4409-1405)
- Mário Casales Schorr (ORCID: https://orcid.org/0000-0003-0437-8054)
- José Luiz Pedrini (ORCID: https://orcid.org/0000-0003-1239-3238)
- Vivian Fontana (ORCID: https://orcid.org/0000-0001-5799-2109)
- Marcelo A. C. Fernandes (ORCID: https://orcid.org/0000-0001-7536-2506)
- Francine Hickmann Nyland
- Caroline Brunetto de Farias (ORCID: https://orcid.org/0000-0002-6435-6626)
- Alessandra Borba Anton de Souza (ORCID: https://orcid.org/0000-0002-6215-7076)
- Martina Lichtenfels (ORCID: https://orcid.org/0000-0003-4783-4646)
- Luiza Kobe (ORCID: https://orcid.org/0000-0002-5644-9654)
Institutions
- Universidade Federal do Rio Grande do Norte (BR)
- Grupo Hospitalar Conceição (BR)
- Pontifícia Universidade Católica do Rio Grande do Sul (BR)
- University of Zielona Góra (PL)
Publication Details
- Journal
- Personalized Medicine
- Published
- 2025-07-16
- DOI
- https://doi.org/10.1080/17410541.2025.2532362
- Primary Topic
- Breast Cancer Treatment Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00