Drug Resistance Prediction Using Deep Learning Techniques on HIV-1 Sequence Data.

ABSTRACT: The fast replication rate and lack of repair mechanisms of human immunodeficiency virus (HIV) contribute to its high mutation frequency, with some mutations resulting in the evolution of resistance to antiretroviral therapies (ART). As such, studying HIV drug resistance allows for real-time evaluation of evolutionary mechanisms. Characterizing the biological process of drug resistance is also critically important for sustained effectiveness of ART. Investigating the link between "black box" deep learning methods applied to this problem and evolutionary principles governing drug resistance has been overlooked to date. Here, we utilized publicly available HIV-1 sequence data and drug resistance assay results for 18 ART drugs to evaluate the performance of three architectures (multilayer perc

SUBMITTER: Steiner MC 

PROVIDER: S-EPMC7290575 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature