Machine learning identifies potential accessory resistance-associated mutations in HIV-1 integrase.

Ssekagiri A, Ssemwanga D, Kateete DP, Jjingo D

Open source

DOI: 10.1186/s12864-026-13243-3
Published: 2026 Aug 3
Container: BMC genomics
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1186/s12864-026-13243-3,
  title = {Machine learning identifies potential accessory resistance-associated mutations in HIV-1 integrase.},
  author = {Ssekagiri A and Ssemwanga D and Kateete DP and Jjingo D},
  year = {2026},
  journal = {BMC genomics},
  doi = {10.1186/s12864-026-13243-3},
  url = {https://doi.org/10.1186/s12864-026-13243-3}
}

RIS

TY  - JOUR
TI  - Machine learning identifies potential accessory resistance-associated mutations in HIV-1 integrase.
AU  - Ssekagiri A
AU  - Ssemwanga D
AU  - Kateete DP
AU  - Jjingo D
PY  - 2026
JO  - BMC genomics
DO  - 10.1186/s12864-026-13243-3
UR  - https://doi.org/10.1186/s12864-026-13243-3
ER  -

APA

A, S., D, S., DP, K., & D, J. (2026). Machine learning identifies potential accessory resistance-associated mutations in HIV-1 integrase.. BMC genomics. https://doi.org/10.1186/s12864-026-13243-3

Source records

pubmed · retrieved 2026-11-10T12:43:12.330Z
europe-pmc · retrieved 2026-11-10T12:43:12.339Z
doaj · retrieved 2026-11-10T12:43:12.336Z