Hypothesis

Embedding principal component (PC)–based batch correction directly into node features and coupling it with degree‑conditioned negative sampling will reduce false‑positive predictions for aging‑relevant drug targets in graph neural network (GNN) models.

Mechanistic Rationale

Current GNNs for drug‑target interaction (DTI) prediction, exemplified by GPS‑DTI, achieve strong performance by integrating Graph Isomorphism Networks with edge features and multi‑head attention【https://pmc.ncbi.nlm.nih.gov/articles/PMC12659342/】. However, they remain vulnerable to confounding from batch effects, hub overfitting, and node feature bias, which inflate scores for highly connected proteins such as mTOR and sirtuins【https://pmc.ncbi.nlm.nih.gov/articles/PMC12659342/】. Causal inference methods outside GNN pipelines show that adjusting for PCs derived from gene expression removes biased network topologies and alters a substantial fraction of Mendelian Randomization inferences【https://pmc.ncbi.nlm.nih.gov/articles/PMC6536645/】. Furthermore, network resampling conditioned on degree and Gene Ontology annotations flags hub‑driven artifacts in protein‑interaction maps【https://pmc.ncbi.nlm.nih.gov/articles/PMC2241843/】. We propose that translating these two strategies into the GNN framework will:

1. Remove systematic variation unrelated to aging biology by projecting raw node embeddings onto the residual space after regressing out top expression PCs.
2. Prevent the model from learning to predict interactions solely based on node degree by generating negative samples that match the degree distribution of positives, thereby penalizing hub‑centric shortcuts.
3. Preserve the expressive power of edge‑feature attention while ensuring that learned associations reflect genuine aging‑specific signal rather than methodological artifacts.

Experimental Design

• Data: Use a curated aging‑target DTI dataset (e.g., DrugAge, Geroprotectors) and a matched background DTI set from non‑aging indications.
• Models: Compare three GNN architectures:
  • Baseline GPS‑DTI (no confounder correction).
  • GPS‑DTI + PC‑adjusted node features (PCs computed from GTEx tissue‑specific expression, regressed out before embedding).
  • GPS‑DTI + PC‑adjusted features + degree‑conditioned negative sampling (negatives sampled to match degree bins of positives).
• Validation: Perform transductive split for internal metrics (AUROC, AUPRC, F1) and an inductive cold‑start split where entire aging‑target proteins are held out for external validation on independent cohorts (e.g., Human Aging Genome Project).
• Controls: Include negative control proteins (randomly shuffled labels) and hub‑ablation experiments (removing top 5% degree nodes) to assess robustness.
• Statistical Test: Use DeLong’s test to compare AUROC across models; significance set at p < 0.01 after Bonferroni correction for multiple comparisons.

Expected Outcomes

• PC adjustment alone will decrease AUROC for hub‑rich targets (e.g., mTOR, SIRT1) by ~0.03–0.05, indicating removal of spurious boost.
• Adding degree‑conditioned negatives will further reduce false‑positive rates, especially in the cold‑start setting, while preserving or improving performance on true aging targets (expected AUROC gain of 0.02–0.04 over baseline).
• The combined model should show a significant enrichment of known geroprotectors among top‑ranked predictions (e.g., FDR < 0.05) compared to baseline.

Potential Pitfalls

• Over‑correction: Removing too many PCs could erase legitimate aging‑related signal; we will monitor explained variance and retain PCs that cumulatively explain <10% of expression variance.
• Degree‑matched negative sampling may become computationally intensive for large networks; we will approximate via stratified sampling.
• Residual confounding from unmeasured factors (e.g., post‑translational modifications) may persist; future work could integrate PTM‑aware edge features.

By directly embedding causal confounder adjustment and hub‑aware negative controls into a state‑of‑the‑art GNN, this hypothesis offers a concrete, falsifiable route to disentangle true aging‑biology signal from methodological noise, thereby improving the reliability of GNN‑driven drug‑target discovery in aging research.