Trial reportScientific reports2019
Accurate Prediction for Antibody Resistance of Clinical HIV-1 Isolates.
Trial report in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed.
- Comprehensive mapping of the virus and host factors that guide the paths of HIV-1 escape from a therapeutic.Cell reports · 2026Article
- Diverse paths to broadly neutralizing antibody escape among HIV-1 strains.Nature microbiology · 2026Article
- HIV-1 virome profiling using HIV-PULSE to guide therapeutic and curative interventions.EBioMedicine · 2026Article
- Genotypic challenges in implementing broadly neutralizing antibody-based long-acting HIV-1 therapies.Communications medicine · 2026Review
- Prediction of HIV-1 sensitivity to broadly neutralizing antibodies using statistical distribution sampling (SDS) technology.BMC bioinformatics · 2026Article
- Autologous neutralizing antibodies and polyfunctional T cells contribute to long-term HIV-1 post-intervention control.Nature immunology · 2026Article
- Predicting viral sensitivity to antibodies using genetic sequences and antibody similarities.PLoS computational biology · 2026Article
- Comprehensive Mapping of the Virus and Host Factors that Guide the Paths of HIV-1 Escape from a Therapeutic.bioRxiv : the preprint server for biology · 2025Article
- BRAVE: a highly accurate method for predicting HIV-1 antibody resistance using large language models for proteins.bioRxiv : the preprint server for biology · 2025Article
- AI Methods Tailored to Influenza, RSV, HIV, and SARS-CoV-2: A Focused Review.Pathogens (Basel, Switzerland) · 2025Review
- Practical Considerations for Variable Screening in the Super Learner.The New England Journal of Statistics in Data Science · 2025Article
- AI applications in HIV research: advances and future directions.Frontiers in microbiology · 2025Review
- Learning patterns of HIV-1 resistance to broadly neutralizing antibodies with reduced subtype bias using multi-task learning.PLoS computational biology · 2024Article
- Machine-learning-assisted high-throughput identification of potent and stable neutralizing antibodies against all four dengue virus serotypes.Scientific reports · 2024Article
- VISH-Pred: an ensemble of fine-tuned ESM models for protein toxicity prediction.Briefings in bioinformatics · 2024Article
- Current methods for detecting and assessing HIV-1 antibody resistance.Frontiers in immunology · 2024Review
- Prevalence of resistance-associated viral variants to the HIV-specific broadly neutralising antibody 10-1074 in a UK bNAb-naïve population.Frontiers in immunology · 2024Article
- Predicting neutralization susceptibility to combination HIV-1 monoclonal broadly neutralizing antibody regimens.PloS one · 2024Article
- Predicting neutralization susceptibility to combination HIV-1 monoclonal broadly neutralizing antibody regimens.bioRxiv : the preprint server for biology · 2023Article
- Predicted resistance to broadly neutralizing antibodies (bnAbs) and associated HIV-1 envelope characteristics among seroconverting adults in Botswana.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Broadly neutralizing antibodies (bNAbs) targeting the HIV-1 envelope glycoprotein (Env) have promising utility in prevention and treatment of HIV-1 infection, and several are currently undergoing clinical trials. Due to the high sequence diversity and mutation rate of HIV-1, viral isolates are often resistant to specific bNAbs. Currently, resistant isolates are commonly identified by time-consuming and expensive in vitro neutralization assays. Here, we report machine learning classifiers that accurately predict resistance of HIV-1 isolates to 33 bNAbs. Notably, our classifiers achieved an overall prediction accuracy of 96% for 212 clinical isolates from patients enrolled in four different clinical trials. Moreover, use of gradient boosting machine - a tree-based machine learning method - enabled us to identify critical features, which had high accordance with epitope residues that distinguished between antibody resistance and sensitivity. The availability of an in silico antibody resistance predictor should facilitate informed decisions of antibody usage and sequence-based monitoring of viral escape in clinical settings.
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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.