Effective resistance predicts reliability in tissue protein networks
Effective Resistance and Graph Neural Network Reliability in Tissue-Specific Interactomes
Machine Learning
Summary
Predicting how proteins work in different body tissues is hard, and it’s important to know when those predictions might be wrong. The authors studied special maps of protein interactions within tissues and found that a measure called effective resistance can help identify where predictions are less reliable. They discovered this measure mostly reflects simple properties of the network but still adds useful insight beyond those. This insight improves understanding of model reliability as predictions get more detailed.
What this means in practice
- •For bioinformatics engineers: Improve reliability assessment in protein function prediction models by incorporating effective resistance to spot uncertain predictions in tissue-specific interaction networks.
- •For systems biology teams: Use effective resistance alongside node degree to better understand network structure effects on predictive errors in tissue-specific protein networks.
Authors
Jianru Shen
Abstract
Protein function annotation needs to know which predictions to distrust, not only what a model predicts. We ask whether tissue-specific interaction structure carries that information. Our candidate signal is effective resistance, used previously to relieve over-squashing by rewiring. Across 24 tissue-specific interactomes it is dominated by inverse degree, and the degeneration deepens as the co-expression filtered network grows, with a Spearman correlation of -0.955. The residual departure from that limit exceeds degree-preserving null graphs in all 24 networks. Controlling for predictive entropy, degree, annotation cardinality, local structure and feature-only difficulty, the residual explains additional per-node loss in 19 of 24 held-out networks once a permutation floor is subtracted, at every depth, and the effect strengthens monotonically with depth. The increment reaches 0.37% of the variance the controls leave unexplained, 5.6 times a permutation floor, against 1.5 times when the model is retrained in a degree-preserving null world. Selective prediction improves negligibly. The signal is reproducible; degree degeneration bounds it.