Interpretability
Cross-source consensus on Interpretability from 1 sources and 6 claims.
1 sources · 6 claims
How it works
Benefits
Comparisons
Highlighted claims
- The study combines Integrated Gradients with graph-branch occlusion for interpretability. — Learning Multi-Relational Graph Representations for DNA Methylation-Based Biological Age Estimation
- Mean absolute Integrated Gradients over feature dimensions gives node importance, and averaging over nodes gives feature importance. — Learning Multi-Relational Graph Representations for DNA Methylation-Based Biological Age Estimation
- Graph importance is estimated by masking each branch and normalizing the absolute prediction change. — Learning Multi-Relational Graph Representations for DNA Methylation-Based Biological Age Estimation
- Methylation value had the highest node-attribute importance. — Learning Multi-Relational Graph Representations for DNA Methylation-Based Biological Age Estimation
- Graph-level interpretability found that the co-methylation graph had higher average importance than same-gene and same-chromosome graphs. — Learning Multi-Relational Graph Representations for DNA Methylation-Based Biological Age Estimation
- CpG island features, next-base-pair attributes, and distance to transcription start site were important model contributors. — Learning Multi-Relational Graph Representations for DNA Methylation-Based Biological Age Estimation