Interpretable Machine Learning Algorithms Identify Inetetamab‐Mediated Metabolic Signatures and Biomarkers in Treating Breast Cancer
In summary, the identification of these two significant differential metabolites holds promise as potential biomarkers for evaluating and predicting inetetamab treatment outcomes in BC, ultimately contributing to the diagnosis of the disease and the discovery of prognostic markers.
Authors
- Binliang Liu (ORCID: https://orcid.org/0000-0002-3692-2272)
- Dehua Liao (ORCID: https://orcid.org/0000-0001-8376-1419)
- Liping Liu (ORCID: https://orcid.org/0000-0003-2943-055X)
- Gang Huang (ORCID: https://orcid.org/0000-0002-5786-7110)
- Ning Xie (ORCID: https://orcid.org/0000-0002-4628-2299)
- Quchang Ouyang
- Jiwen Zhang (ORCID: https://orcid.org/0000-0003-1632-9516)
Institutions
- Central South University (CN)
- Hunan Cancer Hospital (CN)
Publication Details
- Journal
- Journal of Clinical Laboratory Analysis
- Published
- 2024-11-21
- DOI
- https://doi.org/10.1002/jcla.25124
- Citations
- 2
- Primary Topic
- Cancer, Lipids, and Metabolism
- Type
- article
- Field-Weighted Citation Impact
- 0.57