ArticleMicrobiology spectrum2023
Predicting Antigenic Distance from Genetic Data for PRRSV-Type 1: Applications of Machine Learning.
Article in Microbiology spectrum, 2023. 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.
- Translating viral genetic data to PRRSV-2 cross-neutralization using machine learning.Frontiers in immunology · 2026Article
- Opportunities for machine learning to predict cross-neutralization in FMDV serotype O.PLoS computational biology · 2025Article
- Review
- PRRSV-2 variant classification: a dynamic nomenclature for enhanced monitoring and surveillance.mSphere · 2025Article
- Linear epitopes of PRRSV-1 envelope proteins ectodomains are not correlated with broad neutralization.Porcine health management · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The control of porcine reproductive and respiratory syndrome (PRRS) remains a significant challenge due to the genetic and antigenic variability of the causative virus (PRRSV). Predominantly, PRRSV management includes using vaccines and live virus inoculations to confer immunity against PRRSV on farms. While understanding cross-protection among strains is crucial for the continued success of these interventions, understanding how genetic diversity translates to antigenic diversity remains elusive. We developed machine learning algorithms to estimate antigenic distance
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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.