BACKGROUND DNA methylation (DNAm) is an epigenetic regulator of gene expression programs that can be altered by environmental exposures, aging, and in pathogenesis. Traditional analyses that associate DNAm alterations with phenotypes suffer from multiple hypothesis testing and multi-collinearity due to the high-dimensional, continuous, interacting and non-linear nature of the data. Deep learning analyses have shown much promise to study disease heterogeneity. DNAm deep learning approaches have not yet been formalized into user-friendly frameworks for execution, training, and interpreting models. Here, we describe MethylNet, a DNAm deep learning method that can construct embeddings, make predictions, generate new data, and uncover unknown heterogeneity with minimal user supervision. RESULTS The results of our experiments indicate that MethylNet can study cellular differences, grasp higher order information of cancer sub-types, estimate age and capture factors associated with smoking in concordance with known differences. CONCLUSION The ability of MethylNet to capture nonlinear interactions presents an opportunity for further study of unknown disease, cellular heterogeneity and aging processes.

中文翻译：

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MethylNet：用于DNA甲基化分析的自动化和模块化深度学习方法。

背景技术DNA甲基化（DNAm）是基因表达程序的表观遗传调控因子，可因环境暴露，衰老和发病机理而改变。将DNAm改变与表型联系起来的传统分析由于数据的高维，连续，相互作用和非线性性质而遭受多重假设检验和多重共线性的困扰。深度学习分析显示出研究疾病异质性的巨大希望。DNAm深度学习方法尚未正式化为用户友好的框架，用于执行，培训和解释模型。在这里，我们描述了MethylNet，这是一种DNAm深度学习方法，可以构建嵌入，进行预测，生成新数据并在最小的用户监督下发现未知的异构性。结果我们的实验结果表明，MethylNet可以研究细胞差异，掌握癌症亚型的高级信息，根据已知差异估算与吸烟相关的年龄和捕获因素。结论MethylNet捕获非线性相互作用的能力为进一步研究未知疾病，细胞异质性和衰老过程提供了机会。