Machine learning quantifies immuno-virological interactions: a TRIPOD+AI compliant prediction model for HIV-1 salvage therapy outcomes.

Yuan D, Liu Y, Liu S, Peng N, Zhu X, Qian Q, Wang B, Yin Y

DOI: 10.3389/fimmu.2026.1846559
Published: 2026
Container: Frontiers in immunology
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.3389/fimmu.2026.1846559,
  title = {Machine learning quantifies immuno-virological interactions: a TRIPOD+AI compliant prediction model for HIV-1 salvage therapy outcomes.},
  author = {Yuan D and Liu Y and Liu S and Peng N and Zhu X and Qian Q and Wang B and Yin Y},
  year = {2026},
  journal = {Frontiers in immunology},
  doi = {10.3389/fimmu.2026.1846559},
  url = {https://doi.org/10.3389/fimmu.2026.1846559}
}

RIS

TY  - JOUR
TI  - Machine learning quantifies immuno-virological interactions: a TRIPOD+AI compliant prediction model for HIV-1 salvage therapy outcomes.
AU  - Yuan D
AU  - Liu Y
AU  - Liu S
AU  - Peng N
AU  - Zhu X
AU  - Qian Q
AU  - Wang B
AU  - Yin Y
PY  - 2026
JO  - Frontiers in immunology
DO  - 10.3389/fimmu.2026.1846559
UR  - https://doi.org/10.3389/fimmu.2026.1846559
ER  -

APA

D, Y., Y, L., S, L., N, P., X, Z., Q, Q., B, W., & Y, Y. (2026). Machine learning quantifies immuno-virological interactions: a TRIPOD+AI compliant prediction model for HIV-1 salvage therapy outcomes.. Frontiers in immunology. https://doi.org/10.3389/fimmu.2026.1846559

Source records

pubmed · retrieved 2026-09-26T15:15:44.406Z