Evaluating machine learning approaches for host prediction using H3 influenza genomic data

These models allow for rapid and accurate host prediction of H3 IAV datasets from any of the eight IAV segments and provide a solid framework that allows for identification of variants with higher than typical between-species transmission potential. However, results obtained on selected case studies suggest further improvements of the training and validation processes should be considered.

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Publication Details

Journal
PLoS ONE
Published
2025-11-05
DOI
https://doi.org/10.1371/journal.pone.0336142
Primary Topic
Influenza Virus Research Studies
Type
article
Field-Weighted Citation Impact
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article

Evaluating machine learning approaches for host prediction using H3 influenza genomic data

Hoc Tran, Olaf Berke, Zvonimir Poljak, Nicole Ricker
PLoS ONE
Influenza Virus Research Studies
article

Evaluating machine learning approaches for host prediction using H3 influenza genomic data

Hoc Tran, Olaf Berke, Zvonimir Poljak, Nicole Ricker
article en

Abstract

These models allow for rapid and accurate host prediction of H3 IAV datasets from any of the eight IAV segments and provide a solid framework that allows for identification of variants with higher than typical between-species transmission potential. However, results obtained on selected case studies suggest further improvements of the training and validation processes should be considered.

PLoS ONEVol. 20(11)
University of Guelph (CA)
Openalex Percentile: Top 57%
Influenza Virus Research Studies
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