Interrogating the evolution of biological changes at early stages of life
requires longitudinal profiling of molecules, such as DNA methylation, which
can be challenging with children. We introduce a probabilistic and longitudinal
machine learning framework based on multi-mean Gaussian processes (GPs),
accounting for individual and gene correlations across time. This method
provides future predictions of DNA methylation status at different individual
ages while accounting for uncertainty. Our model is trained on a birth cohort
of children with methylation profiled at ages 0-4, and we demonstrated that the
status of methylation sites for each child can be accurately predicted at ages
5-7. We show that methylation profiles predicted by multi-mean GPs can be used
to estimate other phenotypes, such as epigenetic age, and enable comparison to
other health measures of interest. This approach encourages epigenetic studies
to move towards longitudinal design for investigating epigenetic changes during
development, ageing and disease progression.

探究生命早期生物变化的演变需要对分子（如 DNA 甲基化）进行纵向分析，而儿童数据采集可能存在一定挑战。我们引入了一种基于多平均高斯过程（GPs）的概率性和纵向机器学习框架，考虑时间上的个体和基因相关性，提供了在不同个体年龄下 DNA 甲基化状态的未来预测，同时考虑了不确定性。我们的模型是在一个 0-4 岁儿童的出生队列中进行训练的，并且我们证明了在 5-7 岁的年龄段可以准确预测每个儿童的甲基化位点的状态。我们展示了多平均高斯过程预测的甲基化配置可以用于估计其他表型，如表观遗传年龄，并与其他感兴趣的健康指标进行比较。该方法鼓励表观遗传学研究朝向纵向设计，以探究发育、衰老和疾病进展过程中的表观遗传变化。

DNA 甲基化的纵向预测与表观遗传结果预测

Longitudinal prediction of DNA methylation to forecast epigenetic  outcomes

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.

提出了一种新颖的可解释的神经网络 MIRACLE，该网络利用自编码器和多任务学习，整合多个数据集，共同识别 DNA 甲基化中的通用模式，以预测与自身免疫疾病相关的多个表型，并提高了预测准确性。