Machine learning-informed liquid-liquid phase separation for personalized breast cancer treatment assessment
The MDLS model offers a robust and precise tool for predicting breast cancer prognosis and tailoring personalized treatment strategies. Its integration of multi-omics and machine learning highlights its potential clinical applications, particularly in improving the effectiveness of immunotherapy and identifying therapeutic targets for high-MDLS patients.
Authors
- Huan Chen (ORCID: https://orcid.org/0000-0003-3910-3712)
- Jing Hou (ORCID: https://orcid.org/0000-0002-5845-6997)
- Shu Wang (ORCID: https://orcid.org/0000-0002-8651-4795)
- Tao Wang (ORCID: https://orcid.org/0000-0001-7087-4596)
- Jie Xie (ORCID: https://orcid.org/0000-0002-7618-5799)
- Zhuolin Li (ORCID: https://orcid.org/0009-0009-6991-3653)
Institutions
- Guizhou Provincial People's Hospital (CN)
Publication Details
- Journal
- Frontiers in Immunology
- Published
- 2024-11-19
- DOI
- https://doi.org/10.3389/fimmu.2024.1485123
- Citations
- 4
- Primary Topic
- Metabolomics and Mass Spectrometry Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.94