The tissue specificity of DNA methylation refers to the significant differences in DNA methylation patterns in different tissues. This specificity regulates gene expression, thereby supporting the specific functions of each tissue and the maintenance of normal physiological activities. Abnormal tissue-specific patterns of DNA methylation are closely related to age-related diseases. This abnormal methylation pattern affects the regulation of gene expression, which may lead to changes in cell function and promote the occurrence of pathological conditions. By analyzing the differences in these methylation patterns, key CpG sites for disease diagnosis can be effectively screened. The main goal of this paper is to use the characteristics associated with tissue-specific abnormal expression and disease to construct an age-related disease diagnosis model. First, we combined chi-square tests and logistic regression to identify tissue-specific and disease-specific CpG sites, laying the foundation for accurate medical diagnosis, and verified the biological relevance of these CpG sites through enrichment analysis. Then we used the Transformer model to fit these CpG sites and realized the automatic diagnosis of age-related diseases. Our work proves that the tissue specificity of DNA methylation has the potential to diagnose age-related diseases, and proves the scientific nature of our proposed diagnostic method from a biological perspective.
Keywords: CpG site; DNA methylation; chi-square analysis; disease specificity; logistic regression; tissue specificity; transformer model.