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HIV-V3Augur: A Novel Machine Learning Model for Predicting HIV-1 Tropism in Sub-Subtype A6 and CRF63_02A6, Predominant Variants in Russia and Countries of the Former Soviet Union

Research output: Contribution to journal › Article › peer-review

DOI

https://doi.org/10.3390/v18070703
Final published version

Kirill Elfimov
Ludmila Gotfrid
Alina Nokhova
Mariya Gashnikova
Vasiliy Ekushov
Maksim Halikov
Irina Osipova
Dmitriy Baboshko
Andrey Murzin
Ivan Kondeikin
Arina Kiryakina
Aleksey Totmenin
Aleksandr Agaphonov
Natalya Gashnikova

Translated title of the contribution	HIV-V3Augur: Новая модель машинного обучения для прогнозирования тропизма ВИЧ-1 субсубтипа A6 и рекомбинантной формы CRF63_02A6 — преобладающих вариантов в России и странах бывшего СССР
Original language	English
Number of pages	27
Journal	Viruses
Volume	18
Issue number	7
DOIs	https://doi.org/10.3390/v18070703
Publication status	Published - 25 Jun 2026

Research areas

HIV-1/physiology, Viral Tropism, Humans, Russia/epidemiology, HIV Envelope Protein gp120/genetics, HIV Infections/virology, Genotype, Machine Learning, USSR/epidemiology, Predictive Learning Models, Genetic Variation, Cell Line, Prediction Algorithms

OECD FOS+WOS

1.06.ZE VIROLOGY

ID: 83183345