Machine learning-based identification of tumor-infiltrating immune cell-associated model with appealing implications in improving prognosis and immunotherapy response in bladder cancer patients.
- DOI
- 10.3389/fimmu.2023.1171420
- Published
- 2023
- Container
- Frontiers in immunology
- Publisher
- Not recorded
- Open access
- yes
Credibility signals
limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
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Cite this work
BibTeX
@article{allodium:10.3389/fimmu.2023.1171420,
title = {Machine learning-based identification of tumor-infiltrating immune cell-associated model with appealing implications in improving prognosis and immunotherapy response in bladder cancer patients.},
author = {Chen H and Yang W and Ji Z},
year = {2023},
journal = {Frontiers in immunology},
doi = {10.3389/fimmu.2023.1171420},
url = {https://doi.org/10.3389/fimmu.2023.1171420}
}RIS
TY - JOUR TI - Machine learning-based identification of tumor-infiltrating immune cell-associated model with appealing implications in improving prognosis and immunotherapy response in bladder cancer patients. AU - Chen H AU - Yang W AU - Ji Z PY - 2023 JO - Frontiers in immunology DO - 10.3389/fimmu.2023.1171420 UR - https://doi.org/10.3389/fimmu.2023.1171420 ER -
APA
H, C., W, Y., & Z, J. (2023). Machine learning-based identification of tumor-infiltrating immune cell-associated model with appealing implications in improving prognosis and immunotherapy response in bladder cancer patients.. Frontiers in immunology. https://doi.org/10.3389/fimmu.2023.1171420
Source records
- pubmed · retrieved 2026-09-25T22:45:49.133Z