RelAge-GNN
Cross-source consensus on RelAge-GNN from 1 sources and 5 claims.
1 sources · 5 claims
How it works
Benefits
Highlighted claims
- RelAge-GNN builds three separate relational graphs over the same CpG nodes. — Learning Multi-Relational Graph Representations for DNA Methylation-Based Biological Age Estimation
- Each relational graph is processed by an independent graph neural network branch and fused with a learnable node-level gate. — Learning Multi-Relational Graph Representations for DNA Methylation-Based Biological Age Estimation
- The model predicts biological age from methylation and genomic features through node compression, graph readout, and an MLP regression head. — Learning Multi-Relational Graph Representations for DNA Methylation-Based Biological Age Estimation
- RelAge-GNN achieved the strongest chronological-age correlation among the reported models while keeping error rates competitive. — Learning Multi-Relational Graph Representations for DNA Methylation-Based Biological Age Estimation
- RelAge-GNN is presented as better at detecting disease-associated deviations from expected aging trajectories than ordinary regression metrics alone show. — Learning Multi-Relational Graph Representations for DNA Methylation-Based Biological Age Estimation