Article

Identification of HIV-Associated Gene Expression Biomarkers Using Machine Learning and Interpretable Artificial Intelligence

2025-05-12

Abstract excerpt

Despite advances in antiretroviral therapy (ART), the early and accurate diagnosis of Human Immunodeficiency Virus (HIV) infection remains a significant public health challenge. Traditional biomarkers, such as CD4+ T cell counts and viral load, are limited in capturing the complex biological mechanisms underlying HIV pathogenesis. This study proposes a machine learning (ML) and interpretable artificial intelligenc...

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Literature Corpus work: 1d939e04-83c4-5cbd-bfd4-8b0a8f7f5f98
DOI: 10.1101/2025.05.08.652807

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