GraphAge: Unleashing the power of graph neural network to decode epigenetic aging.

ABSTRACT: DNA methylation is a crucial epigenetic marker used in various clocks to predict epigenetic age. However, many existing clocks fail to account for crucial information about CpG sites and their interrelationships, such as co-methylation patterns. We present a novel approach to represent methylation data as a graph, using methylation values and relevant information about CpG sites as nodes, and relationships like co-methylation, same gene, and same chromosome as edges. We then use a graph neural network (GNN) to predict age. Thus our model, GraphAge leverages both the structural and positional information for prediction as well as better interpretation. Although, we had to train in a constrained compute setting, GraphAge still showed competitive performance with a mean absolute error of 3.20

SUBMITTER: Ahmed SS 

PROVIDER: S-EPMC12203532 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature