ArticleiScience2024
Positive-unlabeled learning to infer protection status and identify correlates in vaccine efficacy field trials.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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.
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Who cites it
5 citing papers in PubMed.
- Candidate correlates of protection in the HVTN505 HIV-1 vaccine efficacy trial identified by positive-unlabeled learning.PLoS computational biology · 2025Trial
- Review
- Candidate Correlates of Protection in the HVTN505 HIV-1 Vaccine Efficacy Trial Identified by Positive-Unlabeled Learning.medRxiv : the preprint server for health sciences · 2025Article
- Plasmodium vivax antigen candidate prediction improves with the addition of Plasmodium falciparum data.NPJ systems biology and applications · 2024Article
- Leveraging permutation testing to assess confidence in positive-unlabeled learning applied to high-dimensional biological datasets.BMC bioinformatics · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Correlates of protection (CoPs) are key guideposts that both support vaccine development and licensure as well as improve our understanding of the attributes of immune responses that may directly provide protection. Unfortunately, factors such as low rate of exposure and low efficacy can result in low power to discover correlates in field trials-making it difficult to identify these guideposts for the pathogens against which there is greatest need for further insights. To address this gap, we examine the ability of positive-unlabeled (PU) learning approaches to use immunogenicity data and infection status outcomes to accurately predict protection status. We report a combination of PU bagging and two-step reliable negative techniques that accurately classify the protection status of unlabeled (uninfected) samples from synthetic and real-world humoral immune response profiles in human trials and animal models and lead to the discovery of CoPs that are "missed" using conventional infection status case-control analysis.
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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.