DNA methylation is a crucial regulator of gene transcription and has been linked to various diseases, including autoimmune diseases and cancers. However, diagnostics based on DNA methylation face challenges due to large feature sets and small sample sizes, resulting in overfitting and suboptimal performance. To address these issues, we propose MIRACLE, a novel interpretable neural network that leverages autoencoder-based multi-task learning to integrate multiple datasets 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 layer representing pathway information as the basic unit of heredity. Customized defined MaskedLinear Layer is constrained by site-gene-pathway graph adjacency matrix information, which provides explainability and expresses the site-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 lupus erythematosus, multiple sclerosis, inflammatory bowel disease, psoriasis, and type 1 diabetes, MIRACLE demonstrates robust performance in identifying common functions of DNA methylation across different phenotypes, with higher accuracy in prediction dieseases than baseline methods. By incorporating biological prior knowledge, MIRACLE offers a meaningful and interpretable framework for DNA methylation data analysis in the context of autoimmune diseases.

翻译：DNA甲基化是基因转录的关键调控因子，已被证实与包括自身免疫性疾病和癌症在内的多种疾病相关。然而，基于DNA甲基化的诊断因特征集庞大而样本量小面临挑战，易导致过拟合及性能欠佳。为解决这些问题，我们提出MIRACLE这一新型可解释神经网络，通过基于自编码器的多任务学习整合多个数据集，联合识别DNA甲基化的共同模式。MIRACLE的架构反映了甲基化位点、基因和通路之间的关联，确保生物可解释性与意义性。该网络由编码器和解码器组成，瓶颈层将通路信息表征为遗传基本单元。自定义的MaskedLinear层受位点-基因-通路图邻接矩阵信息约束，既提供可解释性，又显式表达位点-基因-通路的层级结构。从嵌入表示出发，多个多任务分类器分别进行疾病预测。在包含类风湿关节炎、系统性红斑狼疮、多发性硬化症、炎症性肠病、银屑病和1型糖尿病的六个数据集上的测试表明，MIRACLE在识别不同表型间DNA甲基化共同功能方面表现稳健，且疾病预测准确率优于基线方法。通过整合生物学先验知识，MIRACLE为自身免疫疾病背景下的DNA甲基化数据分析提供了有意义且可解释的框架。