A machine learning approach for identifying amino acid signatures in the HIV env gene predictive of dementia.

ABSTRACT: The identification of nucleotide sequence variations in viral pathogens linked to disease and clinical outcomes is important for developing vaccines and therapies. However, identifying these genetic variations in rapidly evolving pathogens adapting to selection pressures unique to each host presents several challenges. Machine learning tools provide new opportunities to address these challenges. In HIV infection, virus replicating within the brain causes HIV-associated dementia (HAD) and milder forms of neurocognitive impairment in 20-30% of patients with unsuppressed viremia. HIV neurotropism is primarily determined by the viral envelope (env) gene. To identify amino acid signatures in the HIV env gene predictive of HAD, we developed a machine learning pipeline using the PART rule-learnin

SUBMITTER: Holman AG 

PROVIDER: S-EPMC3498126 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature