94% of researchers rate our articles as excellent or good
Learn more about the work of our research integrity team to safeguard the quality of each article we publish.
Find out more
BRIEF RESEARCH REPORT article
Front. Bioinform.
Sec. Genomic Analysis
Volume 5 - 2025 | doi: 10.3389/fbinf.2025.1562668
ORF1ab Codon Frequency Model Predicts Host-Pathogen Relationship in Orthocoronavirinae
Provisionally accepted- MRIGlobal, Kansas City, United States
Predicting phenotypic properties of a virus directly from its sequence data is an attractive goal for viral epidemiology. Here, we focus narrowly on the Orthocoronavirinae clade and demonstrate models that are powerfully predictive for a human-pathogen phenotype with 76.74 % average precision and 85.96 % average recall on the withheld test set groups, using only Orf1ab codon frequencies. We show alternative examples for other viral coding sequences and feature representations that do not perform well and discuss what distinguishes the models that are performant. These models point to a small subset of features, specifically 5 codons, that are critical to the success of the models. We discuss and contextualize how this observation may fit within a larger model for the role of translation in virus-host agreement.
Keywords: machine learning, feature selection, Genotype-to-phenotype, Viruses, Bioinformactics
Received: 17 Jan 2025; Accepted: 27 Feb 2025.
Copyright: © 2025 Davis and Russell. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Phillip Evert Davis, MRIGlobal, Kansas City, United States
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.