ArticleHealthcare informatics research2017
Machine Learning to Improve the Effectiveness of ANRS in Predicting HIV Drug Resistance.
Article in Healthcare informatics research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning in HIV Care and Antiretroviral Therapy: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Machine Learning Application in Enhancing HIV Management and Treatment Outcomes: Revolutionizing HIV Infection.Current HIV research · 2026Review
- Machine learning aided multiscale modelling of the HIV-1 infection in the presence of NRTI therapy.PeerJ · 2023Article
- A Computational Approach for the Prediction of Treatment History and the Effectiveness or Failure of Antiretroviral Therapy.International journal of molecular sciences · 2020Article
- Algorithmic prediction of HIV status using nation-wide electronic registry data.EClinicalMedicine · 2019Article
- Fuzzy-multidimensional deep learning for efficient prediction of patient response to antiretroviral therapy.Heliyon · 2019Article
- Prediction Methods of Herbal Compounds in Chinese Medicinal Herbs.Molecules (Basel, Switzerland) · 2018Review
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesHuman immunodeficiency virus infection and acquired immune deficiency syndrome (HIV/AIDS) is one of the major burdens of disease in developing countries, and the standard-of-care treatment includes prescribing antiretroviral drugs. However, antiretroviral drug resistance is inevitable due to selective pressure associated with the high mutation rate of HIV. Determining antiretroviral resistance can be done by phenotypic laboratory tests or by computer-based interpretation algorithms. Computer-based algorithms have been shown to have many advantages over laboratory tests. The ANRS (Agence Nationale de Recherches sur le SIDA) is regarded as a gold standard in interpreting HIV drug resistance using mutations in genomes. The aim of this study was to improve the prediction of the ANRS gold standard in predicting HIV drug resistance.
methodsA genome sequence and HIV drug resistance measures were obtained from the Stanford HIV database (http://hivdb.stanford.edu/). Feature selection was used to determine the most important mutations associated with resistance prediction. These mutations were added to the ANRS rules, and the difference in the prediction ability was measured.
resultsThis study uncovered important mutations that were not associated with the original ANRS rules. On average, the ANRS algorithm was improved by 79% ± 6.6%. The positive predictive value improved by 28%, and the negative predicative value improved by 10%.
conclusionsThe study shows that there is a significant improvement in the prediction ability of ANRS gold standard.
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