| Čoahkkáigeassu: | Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition with a strong genetic
component, but its underlying genetic mechanisms remain poorly understood. This study aims to
investigate the codon usage patterns in ASD-related genes to better understand their role in gene
expression and translational efficiency. Our analysis reveals that ASD genes exhibit a low level of
codon usage bias, suggesting the influence of mutational pressure and translational selection. Machine
learning models, including Random Forest and SVM, classify genes effectively based on codon usage,
highlighting potential regulatory mechanisms. Additionally, our findings show that specific codons
and codon pairs in ASD-related genes may influence protein folding and gene regulation. These
insights not only advance our understanding of ASD's genetic basis but also open doors for developing
targeted therapies that could optimize gene expression in ASD-related pathways.
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