BRIDGE: Bottleneck-Aware Regulator-Set Inference and Diagnosis for Cooperative Gene Regulatory Recovery

2026-07-20

AI Summary

This study addresses the limitation of existing gene regulatory network inference methods, which predominantly focus on pairwise interactions and struggle to accurately identify sets of co-regulators. To overcome this, the authors propose BRIDGE, a novel framework that enables end-to-end inference of complete regulatory sets for the first time, accompanied by the TRACE diagnostic suite to pinpoint pipeline bottlenecks. Key innovations include a mechanism-mismatch stress test designed to prevent information leakage and circular dependencies, and Residual HOS²—a residual higher-order set scoring method that directly models raw expression vectors without handcrafted features. The approach integrates PairS² for candidate generation with HOS²-based reranking. Evaluated across 30 cooperative regulatory settings, BRIDGE achieves a Jaccard similarity of 0.460, recall of 0.597, and doubles the exact recovery rate to 0.113, while reducing candidate set size by 94–97% without compromising performance.

Abstract

Cooperative gene regulation often depends on groups of regulators acting jointly, but most gene regulatory network (GRN) inference methods output pairwise regulator-target rankings. We introduce Bottleneck-Aware Regulator-Set Inference and Diagnosis (BRIDGE), a framework for complete regulator-set recovery, and Targeted Recovery Attribution for Cooperative Evaluation (TRACE), a diagnostic suite that attributes failures to retrieval, set-level scoring, decoding, and evaluation bottlenecks. TRACE includes a leak-free mechanism-mismatch cooperativity stress test in which cooperative targets are generated by random nonlinear mechanisms rather than product interactions. This design avoids feature-mechanism circularity: Residual higher-order set scoring (Residual HOS2) operates on raw expression vectors without handcrafted product-correlation features. Across 30 matched seed-cooperativity settings, Residual HOS2 improves Jaccard similarity from 0.382 to 0.460, recall from 0.522 to 0.597, and exact recovery from 0.053 to 0.113 over a decomposable pairwise set scorer (PairS2), although exact recovery remains low. On SERGIO DS3, oracle retrieval and TRACE show that candidate coverage is necessary but insufficient because set-level misranking remains the dominant source of exact-recovery failure. PairS2 proposal followed by Residual HOS2 reranking reduces HOS2-scored candidate sets by 94-97% while largely preserving exact-recovery behavior. These results distinguish edge ranking, candidate retrieval, set-level scoring, and exact cooperative regulator-set recovery as separate objectives.

Problem

• gene regulatory network
• cooperative regulation
• regulator-set recovery
• set-level scoring
• exact recovery

Innovation

• regulator-set inference
• cooperative gene regulation
• Residual HOS2
• TRACE diagnostics
• bottleneck-aware recovery