Evaluation of Different Machine Learning Approaches to Predict Antigenic Distance Among Newcastle Disease Virus (NDV) Strains

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<sup>2</sup> value of 0.723 compared to only 0.051 for linear models based on genetic distance alone. This significant improvement demonstrates the usefulness of applying flexible ML approaches as a rapid and reliable tool for vaccine selection, minimizing the need for labor-intensive experimental trials. Moreover, the flexibility of this ML framework holds promise for application to other infectious diseases in both animals and humans, particularly in scenarios where rapid response and ethical constraints limit conventional experimental approaches.

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Publication Details

Journal
Viruses
Published
2025-04-14
DOI
https://doi.org/10.3390/v17040567
Citations
4
Primary Topic
Virology and Viral Diseases
Type
article
Field-Weighted Citation Impact
5.42
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article

Evaluation of Different Machine Learning Approaches to Predict Antigenic Distance Among Newcastle Disease Virus (NDV) Strains

Mieke Steensels, Alessia Schivo, Silvia Maniero, Steven Van Borm et al.
4 citations
Viruses
Virology and Viral Diseases
5.42
article

Evaluation of Different Machine Learning Approaches to Predict Antigenic Distance Among Newcastle Disease Virus (NDV) Strains

Mieke Steensels, Alessia Schivo, Silvia Maniero, Steven Van Borm, Mayowa Peter Olabode, Alessio Bortolami, Ruxandra Burlacu, Isabella Monne, Ismaila Shittu, Alice Fusaro, Angela Salomoni, Iuliana Onita, Aleksandar Dodovski, Giovanni Franzo, Francesco Bonfante, I. A. Chvala, Mattia Cecchinato, Mia Kim Torchetti, Chantal J. Snoeck, Vasiliki Christodoulou, Ambra Pastori, Azucena Sánchez Sánchez, Ilaria Zambon
article en
4 citations

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<sup>2</sup> value of 0.723 compared to only 0.051 for linear models based on genetic distance alone. This significant improvement demonstrates the usefulness of applying flexible ML approaches as a rapid and reliable tool for vaccine selection, minimizing the need for labor-intensive experimental trials. Moreover, the flexibility of this ML framework holds promise for application to other infectious diseases in both animals and humans, particularly in scenarios where rapid response and ethical constraints limit conventional experimental approaches.

VirusesVol. 17(4)
University of Padua (IT), Luxembourg Institute of Health (LU), All-Russian Research Institute for Animal Health (RU), Institute for Diagnosis and Animal Health (RO), Istituto Zooprofilattico Sperimentale delle Venezie (IT), Sciensano (Belgium) (BE), Ministerio de Agricultura, Pesca y Alimentación (ES), National Veterinary Research Institute (NG), Ss. Cyril and Methodius University in Skopje (MK)
Decent work and economic growth
Openalex Percentile: Top 5%
Virology and Viral Diseases
5.42
DOIOpen Access PDFOpenAlex
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