发表机构
University of Central Florida（中佛罗里达大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对协同基因调控依赖联合作用调控因子组，但多数基因调控网络推断方法输出两两调控因子-靶点排名的问题，引入BRIDGE框架及TRACE诊断套件，介绍其核心机制，指出该方法提升了多项指标，区分了相关不同目标。
AI 中文摘要
协同基因调控通常依赖于联合作用的调控因子组，但大多数基因调控网络（GRN）推断方法输出的是两两调控因子-靶点排名。我们引入了瓶颈感知调控因子集推断与诊断（BRIDGE），这是一个用于完整调控因子集恢复的框架，以及用于协同评估的靶向恢复归因（TRACE），这是一个将失败归因于检索、集级评分、解码和评估瓶颈的诊断套件。TRACE包括一种无泄漏机制——不匹配协同应力测试，其中协同靶点由随机非线性机制而非乘积相互作用生成。这种设计避免了特征-机制循环：残差高阶集评分（Residual HOS2）在原始表达向量上运行，无需手工制作的乘积相关特征。在30个匹配的种子协同设置中，与可分解的两两集评分器（PairS2）相比，Residual HOS2将Jaccard相似度从0.382提高到0.460，召回率从0.522提高到0.597，精确恢复率从0.053提高到0.113，尽管精确恢复率仍然较低。在SERGIO DS3上，神谕检索和TRACE表明候选覆盖率是必要的但不充分，因为集级错误排名仍然是精确恢复失败的主要来源。PairS2提议后接Residual HOS2重新排名可将HOS2评分的候选集减少94-97%，同时在很大程度上保留精确恢复行为。这些结果区分了边排名、候选检索、集级评分和精确协同调控因子集恢复作为单独的目标。
英文摘要
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.
