ArticlePLoS computational biology2014
IDEPI: rapid prediction of HIV-1 antibody epitopes and other phenotypic features from sequence data using a flexible machine learning platform.
Article in PLoS computational biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed, 45 citations in OpenAlex.
- Accurate Prediction for Antibody Resistance of Clinical HIV-1 Isolates.Scientific reports · 2019Trial
- AI-driven epitope prediction: a system review, comparative analysis, and practical guide for vaccine development.NPJ vaccines · 2025Review
- 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
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
- Current methods for detecting and assessing HIV-1 antibody resistance.Frontiers in immunology · 2024Review
- Artificial intelligence in assisting pathogenic microorganism diagnosis and treatment: a review of infectious skin diseases.Frontiers in microbiology · 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
- To prescreen or not to prescreen for broadly neutralizing antibody sensitivity in HIV cure-related trials.Journal of virus eradication · 2023Article
- Prediction of HIV sensitivity to monoclonal antibodies using aminoacid sequences and deep learning.Bioinformatics (Oxford, England) · 2022Article
- Artificial Intelligence in Vaccine and Drug Design.Methods in molecular biology (Clifton, N.J.) · 2022Article
- A Study of the Recent Trends of Immunology: Key Challenges, Domains, Applications, Datasets, and Future Directions.Sensors (Basel, Switzerland) · 2021Review
- Predicting Antibody Neutralization Efficacy in Hypermutated Epitopes Using Monte Carlo Simulations.Polymers · 2020Article
- HIV-1 Neutralizing Antibody Signatures and Application to Epitope-Targeted Vaccine Design.Cell host & microbe · 2019Article
- Prediction of HIV-1 sensitivity to broadly neutralizing antibodies shows a trend towards resistance over time.PLoS computational biology · 2017Article
- Antibody-virus co-evolution in HIV infection: paths for HIV vaccine development.Immunological reviews · 2017Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
Since its identification in 1983, HIV-1 has been the focus of a research effort unprecedented in scope and difficulty, whose ultimate goals--a cure and a vaccine--remain elusive. One of the fundamental challenges in accomplishing these goals is the tremendous genetic variability of the virus, with some genes differing at as many as 40% of nucleotide positions among circulating strains. Because of this, the genetic bases of many viral phenotypes, most notably the susceptibility to neutralization by a particular antibody, are difficult to identify computationally. Drawing upon open-source general-purpose machine learning algorithms and libraries, we have developed a software package IDEPI (IDentify EPItopes) for learning genotype-to-phenotype predictive models from sequences with known phenotypes. IDEPI can apply learned models to classify sequences of unknown phenotypes, and also identify specific sequence features which contribute to a particular phenotype. We demonstrate that IDEPI achieves performance similar to or better than that of previously published approaches on four well-studied problems: finding the epitopes of broadly neutralizing antibodies (bNab), determining coreceptor tropism of the virus, identifying compartment-specific genetic signatures of the virus, and deducing drug-resistance associated mutations. The cross-platform Python source code (released under the GPL 3.0 license), documentation, issue tracking, and a pre-configured virtual machine for IDEPI can be found at https://github.com/veg/idepi.
Indexed as
Identifiers
What OpenQuestion holds
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.