ArticleProceedings of the National Academy of Sciences of the United States of America2006
Genotypic predictors of human immunodeficiency virus type 1 drug resistance.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 84 papers, 1 of them a synthesis that pooled it.
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Who cites it
84 citing papers in PubMed, 1 synthesis or guideline pooled it, 256 citations in OpenAlex.
- Machine Learning in HIV Care and Antiretroviral Therapy: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Article
- Protein language model embeddings improve HIV drug resistance prediction: a comprehensive benchmark with attention-based interpretability.Bioinformatics (Oxford, England) · 2026Article
- Benchmarking Machine Learning Models for HIV-1 Protease Inhibitor Resistance Prediction: Impact of Data Set Construction and Feature Representation.Journal of chemical information and modeling · 2025Article
- The Phenomenon of Antiretroviral Drug Resistance in the Context of Human Immunodeficiency Virus Treatment: Dynamic and Ever Evolving Subject Matter.Biomedicines · 2024Review
- Web Service for HIV Drug Resistance Prediction Based on Analysis of Amino Acid Substitutions in Main Drug Targets.Viruses · 2023Article
- Prevalence and Structure of HIV-1 Drug Resistance to Antiretrovirals in the Volga Federal District in 2008-2019.Viruses · 2022Article
- Statistical Learning and Uncommon Soil Microbiota Explain Biogeochemical Responses after Wildfire.Applied and environmental microbiology · 2022Article
- Absence of Resistance Mutations in the Integrase Coding Region among ART-Experienced Patients in the Republic of the Congo.Microorganisms · 2021Article
- Article
- Revealing the Mutation Patterns of Drug-Resistant Reverse Transcriptase Variants of Human Immunodeficiency Virus through Proteochemometric Modeling.Biomolecules · 2021Article
- Article
- Predicting HIV drug resistance using weighted machine learning method at target protein sequence-level.Molecular diversity · 2021Article
- Identification of an Antiretroviral Small Molecule That Appears To Be a Host-Targeting Inhibitor of HIV-1 Assembly.Journal of virology · 2021Article
- Highly drug-resistant HIV-1 protease reveals decreased intra-subunit interactions due to clusters of mutations.The FEBS journal · 2020Article
- An Innovative Sequence-to-Structure-Based Approach to Drug Resistance Interpretation and Prediction: The Use of Molecular Interaction Fields to Detect HIV-1 Protease Binding-Site Dissimilarities.Frontiers in chemistry · 2020Article
- HIV drug resistance prediction with weighted categorical kernel functions.BMC bioinformatics · 2019Article
- Assessing Tuning Parameter Selection Variability in Penalized Regression.Technometrics : a journal of statistics for the physical, chemical, and engineering sciences · 2019Article
- Lasso regularization for left-censored Gaussian outcome and high-dimensional predictors.BMC medical research methodology · 2018Article
- A Computational Approach for the Prediction of HIV Resistance Based on Amino Acid and Nucleotide Descriptors.Molecules (Basel, Switzerland) · 2018Article
24 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors at 5 institutions in 2 countries.
Funding
Abstract
Understanding the genetic basis of HIV-1 drug resistance is essential to developing new antiretroviral drugs and optimizing the use of existing drugs. This understanding, however, is hampered by the large numbers of mutation patterns associated with cross-resistance within each antiretroviral drug class. We used five statistical learning methods (decision trees, neural networks, support vector regression, least-squares regression, and least angle regression) to relate HIV-1 protease and reverse transcriptase mutations to in vitro susceptibility to 16 antiretroviral drugs. Learning methods were trained and tested on a public data set of genotype-phenotype correlations by 5-fold cross-validation. For each learning method, four mutation sets were used as input features: a complete set of all mutations in > or =2 sequences in the data set, the 30 most common data set mutations, an expert panel mutation set, and a set of nonpolymorphic treatment-selected mutations from a public database linking protease and reverse transcriptase sequences to antiretroviral drug exposure. The nonpolymorphic treatment-selected mutations led to the best predictions: 80.1% accuracy at classifying sequences as susceptible, low/intermediate resistant, or highly resistant. Least angle regression predicted susceptibility significantly better than other methods when using the complete set of mutations. The three regression methods provided consistent estimates of the quantitative effect of mutations on drug susceptibility, identifying nearly all previously reported genotype-phenotype associations and providing strong statistical support for many new associations. Mutation regression coefficients showed that, within a drug class, cross-resistance patterns differ for different mutation subsets and that cross-resistance has been underestimated.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.