Publication Date
Spring 2025
Degree Type
Master's Project
Degree Name
Master of Science in Bioinformatics (MSBI)
Department
Computer Science
First Advisor
Dr. William Andreopoulos
Second Advisor
Dr. Leila Khatib
Third Advisor
Dr. Wendy Lee
Keywords
Mixture of Experts (MoE), RNA structural prediction, Riboswitches, Synthetic Biology, Machine learning
Abstract
Urban water sewage is a potential health concern due to its possibility to spread contagious RNA viruses such as Coxsackievirus B3. However, detection of viral particles remains challenging because of low viral concentrations in wastewater and high mutation rates of the RNA virus. To address this, this study proposes a novel viral detection method using synthetic riboswitches that bind to the target virus and trigger a reporter gene, amplifying the detection signals. 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. This model used a sparsely gated Mixture of Expert (MoE) layer to route mixed input to specialized experts, achieving excellent generalization performance. Future work includes improving the biological relevance and interpretability of the MoE model.
Recommended Citation
Long, Hainian Audrey, "riboMoE: An Application of Mixture of Experts on Artificial Riboswitch Classification" (2025). Master's Projects. 1510.
DOI: https://doi.org/10.31979/etd.yytx-j2ec
https://scholarworks.sjsu.edu/etd_projects/1510
