ArticleViruses2025
Evaluation of Different Machine Learning Approaches to Predict Antigenic Distance Among Newcastle Disease Virus (NDV) Strains.
Article in Viruses, 2025. 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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
5 citing papers in PubMed.
- Emerging and Re-Emerging Viral Infections in Poultry: Integrating Traditional and AI-Based Control Strategies.Current microbiology · 2026Review
- Intrinsic dataset features drive mutational effect prediction by protein language models.bioRxiv : the preprint server for biology · 2026Article
- Host-Microbe Interactions: Prospects of Machine Learning and Deep Learning Technologies in Animal Viral Disease Management.Veterinary sciences · 2025Review
- Field outbreak investigation and immunoinformatic analysis suggest potential immune evasion by Newcastle disease virus Sub-Genotype XIV.2 in Nigeria.Scientific reports · 2025Article
- The Role of Artificial Intelligence in Herpesvirus Detection, Transmission, and Predictive Modeling: With a Special Focus on Marek's Disease Virus.Pathogens (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
23 authors.
Funding
Abstract
Newcastle disease virus (NDV) continues to present a significant challenge for vaccination due to its rapid evolution and the emergence of new variants. Although molecular and sequence data are now quickly and inexpensively produced, genetic distance rarely serves as a good proxy for cross-protection, while experimental studies to assess antigenic differences are time consuming and resource intensive. In response to these challenges, this study explores and compares several machine learning (ML) methods to predict the antigenic distance between NDV strains as determined by hemagglutination-inhibition (HI) assays. By analyzing F and HN gene sequences alongside corresponding amino acid features, we developed predictive models aimed at estimating antigenic distances. Among the models evaluated, the random forest (RF) approach outperformed traditional linear models, achieving a predictive accuracy with an R
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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 Socratic graph.