ArticleInternational journal of molecular sciences2016
Learning the Relationship between the Primary Structure of HIV Envelope Glycoproteins and Neutralization Activity of Particular Antibodies by Using Artificial Neural Networks.
Article in International journal of molecular sciences, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 26 citations in OpenAlex.
- Accurate Prediction for Antibody Resistance of Clinical HIV-1 Isolates.Scientific reports · 2019Trial
- Predicting viral sensitivity to antibodies using genetic sequences and antibody similarities.PLoS computational biology · 2026Article
- BRAVE: a highly accurate method for predicting HIV-1 antibody resistance using large language models for proteins.bioRxiv : the preprint server for biology · 2025Article
- Practical Considerations for Variable Screening in the Super Learner.The New England Journal of Statistics in Data Science · 2025Article
- Learning patterns of HIV-1 resistance to broadly neutralizing antibodies with reduced subtype bias using multi-task learning.PLoS computational biology · 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
- Bioinformatics Tools for the Analysis of Active Compounds Identified in Ranunculaceae Species.Pharmaceuticals (Basel, Switzerland) · 2023Article
- Repurposing anti-inflammatory drugs for fighting planktonic and biofilm growth. New carbazole derivatives based on the NSAID carprofen: synthesis,Frontiers in cellular and infection microbiology · 2023Article
- Prediction of HIV sensitivity to monoclonal antibodies using aminoacid sequences and deep learning.Bioinformatics (Oxford, England) · 2022Article
- Predicting Antibody Neutralization Efficacy in Hypermutated Epitopes Using Monte Carlo Simulations.Polymers · 2020Article
- Article
- Prediction of HIV-1 sensitivity to broadly neutralizing antibodies shows a trend towards resistance over time.PLoS computational biology · 2017Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The dependency between the primary structure of HIV envelope glycoproteins (ENV) and the neutralization data for given antibodies is very complicated and depends on a large number of factors, such as the binding affinity of a given antibody for a given ENV protein, and the intrinsic infection kinetics of the viral strain. This paper presents a first approach to learning these dependencies using an artificial feedforward neural network which is trained to learn from experimental data. The results presented here demonstrate that the trained neural network is able to generalize on new viral strains and to predict reliable values of neutralizing activities of given antibodies against HIV-1.
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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.