| Περίληψη: | Ischaemic strokes account for the majority of strokes, which are a leading cause of death and
permanent disability globally. In order to identify molecular patterns connected to
disease-specific gene expression, this study investigates codon use bias in stroke-associated
genes. With the aid of statistical testing and machine learning models, we examined nucleotide
composition, codon usage indices, and Relative Synonymous Codon Usage (RSCU) using
genomic data from BrainBase and HRT Atlas.The findings showed a strong bias in favour of
GC-rich codons, especially at the third codon position, indicating adaptive selection for
improved translational efficiency and mRNA stability under stress. Using only codon
characteristics, machine learning classifiers—Random Forest in particular—were able to
differentiate between genes linked to stroke and those involved in housekeeping. These results
point to codon use bias as a possible molecular indicator of stroke, providing encouraging paths
for the identification of biomarkers and better genomic categorisation.
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