Open access
Aug 2026
Machine learning quantifies immuno-virological interactions: a TRIPOD+AI compliant prediction model for HIV-1 salvage therapy outcomes
The developed XGBoost model overcomes the limitations of traditional linear scoring and achieves precise prediction of HIV salvage therapy outcomes by quantifying immune modulatory effects and therapeutic exhaustion markers.
De-Fu Yuan, Yang-Yang Liu, Shan-Shan Liu et al.
· Frontiers in Immunology · 0 citations