Integration of 117 machine learning algorithms and single-cell transcriptomics identifies macrophage polarization and ER stress signatures for cancer prognosis and precision therapy

Shengrong Long, Kewei Xiao, Zhipeng Hao

Open source

DOI: 10.1007/s12672-026-05126-6
Published: 2026-04-30
Container: Discover Oncology
Publisher: Springer Science and Business Media LLC
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1007/s12672-026-05126-6,
  title = {Integration of 117 machine learning algorithms and single-cell transcriptomics identifies macrophage polarization and ER stress signatures for cancer prognosis and precision therapy},
  author = {Shengrong Long and Kewei Xiao and Zhipeng Hao},
  year = {2026},
  journal = {Discover Oncology},
  doi = {10.1007/s12672-026-05126-6},
  url = {https://doi.org/10.1007/s12672-026-05126-6}
}

RIS

TY  - JOUR
TI  - Integration of 117 machine learning algorithms and single-cell transcriptomics identifies macrophage polarization and ER stress signatures for cancer prognosis and precision therapy
AU  - Shengrong Long
AU  - Kewei Xiao
AU  - Zhipeng Hao
PY  - 2026
JO  - Discover Oncology
DO  - 10.1007/s12672-026-05126-6
UR  - https://doi.org/10.1007/s12672-026-05126-6
ER  -

APA

Long, S., Xiao, K., & Hao, Z. (2026). Integration of 117 machine learning algorithms and single-cell transcriptomics identifies macrophage polarization and ER stress signatures for cancer prognosis and precision therapy. Discover Oncology. https://doi.org/10.1007/s12672-026-05126-6

Source records

crossref · retrieved 2026-09-25T12:00:41.972Z