ArticleBioinformatics (Oxford, England)2021
Super LeArner Prediction of NAb Panels (SLAPNAP): a containerized tool for predicting combination monoclonal broadly neutralizing antibody sensitivity.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Optimizing clinical dosing of combination broadly neutralizing antibodies for HIV prevention.PLoS computational biology · 2022Pooled it
- 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
- Predicting viral sensitivity to antibodies using genetic sequences and antibody similarities.PLoS computational biology · 2026Article
- Practical Considerations for Variable Screening in the Super Learner.The New England Journal of Statistics in Data Science · 2025Article
- Data fusion using weakly aligned sources.Journal of the American Statistical Association · 2025Article
- Importance of variables from different time frames for predicting self-harm using health system data.Journal of biomedical informatics · 2024Article
- Protocol for a randomized controlled trial with a stepped care approach, utilizing PrEP navigation with and without contingency management, for transgender women and sexual minority men with a substance use disorder: Assistance Services Knowledge-PrEP (A.S.K.-PrEP).Addiction science & clinical practice · 2024Article
- Learning patterns of HIV-1 resistance to broadly neutralizing antibodies with reduced subtype bias using multi-task learning.PLoS computational biology · 2024Article
- Importance of variables from different time frames for predicting self-harm using health system data.medRxiv : the preprint server for health sciences · 2024Article
- Broadly neutralizing antibodies for HIV prevention: a comprehensive review and future perspectives.Clinical microbiology reviews · 2024Review
- Prevention efficacy of the broadly neutralizing antibody VRC01 depends on HIV-1 envelope sequence features.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Current methods for detecting and assessing HIV-1 antibody resistance.Frontiers in immunology · 2024Review
- 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
- Application of the SLAPNAP statistical learning tool to broadly neutralizing antibody HIV prevention research.iScience · 2023Article
- Prediction of HIV sensitivity to monoclonal antibodies using aminoacid sequences and deep learning.Bioinformatics (Oxford, England) · 2022Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
motivationA single monoclonal broadly neutralizing antibody (bnAb) regimen was recently evaluated in two randomized trials for prevention efficacy against HIV-1 infection. Subsequent trials will evaluate combination bnAb regimens (e.g. cocktails, multi-specific antibodies), which demonstrate higher potency and breadth in vitro compared to single bnAbs. Given the large number of potential regimens, methods for down-selecting these regimens into efficacy trials are of great interest.
resultsWe developed Super LeArner Prediction of NAb Panels (SLAPNAP), a software tool for training and evaluating machine learning models that predict in vitro neutralization sensitivity of HIV Envelope (Env) pseudoviruses to a given single or combination bnAb regimen, based on Env amino acid sequence features. SLAPNAP also provides measures of variable importance of sequence features. By predicting bnAb coverage of circulating sequences, SLAPNAP can improve ranking of bnAb regimens by their potential prevention efficacy. In addition, SLAPNAP can improve sieve analysis by defining sequence features that impact bnAb prevention efficacy. AVAILABILITYAND IMPLEMENTATION: SLAPNAP is a freely available docker image that can be downloaded from DockerHub (https://hub.docker.com/r/slapnap/slapnap). Source code and documentation are available at GitHub (https://github.com/benkeser/slapnap and https://benkeser.github.io/slapnap/).
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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.