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DNA methylation is a crucial regulator of gene transcription and has beenlinked to various diseases, including autoimmune diseases and cancers. However,diagnostics based on DNA methylation face challenges due to large feature setsand small sample sizes, resulting in overfitting and suboptimal performance. Toaddress these issues, we propose MIRACLE, a novel interpretable neural networkthat leverages autoencoder-based multi-task learning to integrate multipledatasets and jointly identify common patterns in DNA methylation.  MIRACLE's architecture reflects the relationships between methylation sites,genes, and pathways, ensuring biological interpretability and meaningfulness.The network comprises an encoder and a decoder, with a bottleneck layerrepresenting pathway information as the basic unit of heredity. Customizeddefined MaskedLinear Layer is constrained by site-gene-pathway graph adjacencymatrix information, which provides explainability and expresses thesite-gene-pathway hierarchical structure explicitly. And from the embedding,there are different multi-task classifiers to predict diseases.  Tested on six datasets, including rheumatoid arthritis, systemic lupuserythematosus, multiple sclerosis, inflammatory bowel disease, psoriasis, andtype 1 diabetes, MIRACLE demonstrates robust performance in identifying commonfunctions of DNA methylation across different phenotypes, with higher accuracyin prediction dieseases than baseline methods. By incorporating biologicalprior knowledge, MIRACLE offers a meaningful and interpretable framework forDNA methylation data analysis in the context of autoimmune diseases.

MIRACLE: Multi-task Learning based Interpretable Regulation of Autoimmune Diseases through Common Latent Epigenetics