Machine Learning Bolsters Evidence That D1, Nef, and Tat Influence HIV Reservoir Dynamics.

ABSTRACT:

Background

The primary hurdle to curing HIV is due to the establishment of a reservoir early in infection. In an effort to find new treatment strategies, we and others have focused on understanding the selection pressures exerted on the reservoir by studying how proviral sequences change over time.

Methods

To gain insights into the dynamics of the HIV reservoir we analyzed longitudinal near full-length sequences from 7 people living with HIV between 1 and 20 years following the initiation of antiretroviral treatment. We used this data to employ Bayesian mixed effects models to characterize the decay of the reservoir using single-phase and multiphasic decay models based on near full-length sequencing. In addition, we developed a machine-learning approach utilizing logistic re

SUBMITTER: Cannon L 

PROVIDER: S-EPMC10827039 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature