Characterization of artificial riboswitches for Coxsackievirus B3 detection using machine learning
- 1Department of Computer Science, San Jose State University
Abstract
With the increase of recycled water to offset water demand, the potential possibility to spread contagious RNA viruses, such as Coxsackievirus B3, increases. However, detection of viral particles remains challenging because of low viral concentrations in wastewater and high mutation rates of the RNA virus. Robust monitoring is needed with low cost and low infrastructure technologies to increase accessibility of monitoring technologies worldwide. Novel viral detection methods have been developed, based on synthetic riboswitches that bind to the target virus and trigger a reporter gene, thus amplifying the detection signals. Such monitoring technologies can leverage machine learning to optimize the candidate nucleic acid sequences used for detection. To support the design of effective riboswitches, we present a machine learning model for classifying riboswitch performance, integrating RNA sequence data with secondary structural features based on free energy calculations and parameters that represent single strandedness. This model uses a sparsely gated Mixture of Experts (MoE) architecture to route sequence and thermodynamic features to specialized experts, achieving strong generalization performance across cross-validation and held-out testing. When evaluated against baseline Decision Tree, Random Forest, Gaussian Naive Bayes, and Dense Neural Network classifiers, the MoE model demonstrated near-zero classification errors. Furthermore, post-hoc feature importance and k-mer analyzes reveal that near-perfect predictive performance in silico is strongly driven by sequence constructs in addition to generalizable folding mechanics. An ablation study of numerical feature and sequence inputs showed that while the Dense Neural Network also achieved high accuracy across all ablation conditions, the MoE architecture was retained as the primary model because its specialized subnetworks do not require all model parameters to be activated offering a potential computational advantage.
Related
- Multiplexed mammalian gene control via modular splicing riboswitches
- Theophylline-dependent synthetic riboswitches enable tightly regulated inducible gene expression in tobacco plastids
- Early Detection of Red Palm Weevil Infestation: A Systematic Review of Sensing Technologies, Artificial Intelligence, and Field Readiness
- A comparison of artificial neural networks and partial least squares modelling for the rapid detection of the microbial spoilage of beef fillets based on Fourier transform infrared spectral fingerprints
- Genomic epidemiology of coxsackievirus A24 variant during the 2024 acute hemorrhagic conjunctivitis outbreak in Coastal Kenya
BibTeX
@misc{auyong2026characterization,
title = {{Characterization of artificial riboswitches for Coxsackievirus B3 detection using machine learning}},
author = {J. Auyong and H. A. Long and S. Hu and J. Jacob and K. Chan and B. Gupta and A. Mengistu and M. Tokuhara and L. Khatib and W. B. Andreopoulos},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.04.756577},
url = {https://www.biorxiv.org/content/10.64898/2026.10.04.756577v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Characterization of artificial riboswitches for Coxsackievirus B3 detection using machine learning
AU - J. Auyong
AU - H. A. Long
AU - S. Hu
AU - J. Jacob
AU - K. Chan
AU - B. Gupta
AU - A. Mengistu
AU - M. Tokuhara
AU - L. Khatib
AU - W. B. Andreopoulos
PY - 2026
DA - 2026/10/04
PB - bioRxiv
DO - 10.64898/2026.10.04.756577
UR - https://www.biorxiv.org/content/10.64898/2026.10.04.756577v1
AB - With the increase of recycled water to offset water demand, the potential possibility to spread contagious RNA viruses, such as Coxsackievirus B3, increases. However, detection of viral particles remains challenging because of low viral concentrations in wastewater and high mutation rates of the RNA virus. Robust monitoring is needed with low cost and low infrastructure technologies to increase accessibility of monitoring technologies worldwide. Novel viral detection methods have been developed, based on synthetic riboswitches that bind to the target virus and trigger a reporter gene, thus amplifying the detection signals. Such monitoring technologies can leverage machine learning to optimize the candidate nucleic acid sequences used for detection. To support the design of effective riboswitches, we present a machine learning model for classifying riboswitch performance, integrating RNA sequence data with secondary structural features based on free energy calculations and parameters that represent single strandedness. This model uses a sparsely gated Mixture of Experts (MoE) architecture to route sequence and thermodynamic features to specialized experts, achieving strong generalization performance across cross-validation and held-out testing. When evaluated against baseline Decision Tree, Random Forest, Gaussian Naive Bayes, and Dense Neural Network classifiers, the MoE model demonstrated near-zero classification errors. Furthermore, post-hoc feature importance and k-mer analyzes reveal that near-perfect predictive performance in silico is strongly driven by sequence constructs in addition to generalizable folding mechanics. An ablation study of numerical feature and sequence inputs showed that while the Dense Neural Network also achieved high accuracy across all ablation conditions, the MoE architecture was retained as the primary model because its specialized subnetworks do not require all model parameters to be activated offering a potential computational advantage.
ER -