ArticleProceedings of the National Academy of Sciences of the United States of America2002
Diversity and complexity of HIV-1 drug resistance: a bioinformatics approach to predicting phenotype from genotype.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2002. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers, 1 of them a synthesis that pooled it.
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Who cites it
69 citing papers in PubMed, 1 synthesis or guideline pooled it, 259 citations in OpenAlex.
- Machine Learning in HIV Care and Antiretroviral Therapy: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Accurate Prediction for Antibody Resistance of Clinical HIV-1 Isolates.Scientific reports · 2019Trial
- Forecasting drug resistant HIV protease evolution.PLoS computational biology · 2026Article
- Phylogenetics and molecular evolution to understand and curb the HIV pandemic.Nature reviews. Microbiology · 2026Review
- 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
- Explainable Artificial Intelligence in the Field of Drug Research.Drug design, development and therapy · 2025Review
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- Article
- Revealing the Mutation Patterns of Drug-Resistant Reverse Transcriptase Variants of Human Immunodeficiency Virus through Proteochemometric Modeling.Biomolecules · 2021Article
- Predicting HIV drug resistance using weighted machine learning method at target protein sequence-level.Molecular diversity · 2021Article
- Prediction of HIV drug resistance based on the 3D protein structure: Proposal of molecular field mapping.PloS one · 2021Article
- Accurate Prediction of Inhibitor Binding to HIV-1 Protease Using CANDOCK.Frontiers in chemistry · 2021Article
- Transforming the study of organisms: Phenomic data models and knowledge bases.PLoS computational biology · 2020Review
- Article
- HIV drug resistance prediction with weighted categorical kernel functions.BMC bioinformatics · 2019Article
- Mapping the drivers of within-host pathogen evolution using massive data sets.Nature communications · 2019Article
- Article
- A Computational Approach for the Prediction of HIV Resistance Based on Amino Acid and Nucleotide Descriptors.Molecules (Basel, Switzerland) · 2018Article
- The dawn of precision medicine in HIV: state of the art of pharmacotherapy.Expert opinion on pharmacotherapy · 2018Review
- HIV Resistance Prediction to Reverse Transcriptase Inhibitors: Focus on Open Data.Molecules (Basel, Switzerland) · 2018Review
9 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
Drug resistance testing has been shown to be beneficial for clinical management of HIV type 1 infected patients. Whereas phenotypic assays directly measure drug resistance, the commonly used genotypic assays provide only indirect evidence of drug resistance, the major challenge being the interpretation of the sequence information. We analyzed the significance of sequence variations in the protease and reverse transcriptase genes for drug resistance and derived models that predict phenotypic resistance from genotypes. For 14 antiretroviral drugs, both genotypic and phenotypic resistance data from 471 clinical isolates were analyzed with a machine learning approach. Information profiles were obtained that quantify the statistical significance of each sequence position for drug resistance. For the different drugs, patterns of varying complexity were observed, including between one and nine sequence positions with substantial information content. Based on these information profiles, decision tree classifiers were generated to identify genotypic patterns characteristic of resistance or susceptibility to the different drugs. We obtained concise and easily interpretable models to predict drug resistance from sequence information. The prediction quality of the models was assessed in leave-one-out experiments in terms of the prediction error. We found prediction errors of 9.6-15.5% for all drugs except for zalcitabine, didanosine, and stavudine, with prediction errors between 25.4% and 32.0%. A prediction service is freely available at http://cartan.gmd.de/geno2pheno.html.
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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.