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
针对有限RISC池守恒缺失问题，提出可微平衡层，通过理论分析和档案数据审计，证明耦合在现有数据中无额外预测价值，贡献守恒算子和测试所需信息。
AI 中文摘要
当配对引导RNA-转录本分数被独立解释为占用率时，它们并不强制有限引导RNA负载的RISC池的守恒。我们构建了一个可微的标量平衡层：一个具有唯一正根和精确隐式梯度的守恒方程。它产生了重分布定理、合格的高资源极限、对检索近似的分析，以及一个条件秩不变性结果：在同一构建的同一剂量下，按分数占用率排序无法区分平衡与独立评分。因此，我们审计了该命题留下的两个实验——剂量和跨情境——在档案脱靶数据上。校正的热力学亲和力与测量的抑制在有效预测器所需方向上弱相关，但配对置换检验和构建簇自助法并未确立耦合带来的额外预测价值：在它们不同的置换零假设基线中幸存的是\GapNet{}，即平衡关联的簇标准误差的描述性\GapNetOverSE{}。剂量拟合是异质的，且经常违反模型隐含的指数约束，该约束是超线性的而非次线性的，因此这些数据无法识别竞争参数。对竞争集的可饱和压缩在保留的引导家族上保持了两个准确性目标，但在所测规模上并不更快。贡献在于一个可复用的守恒算子以及测试它所需的实验信息。本研究的代码可在该https URL获取。
英文摘要
Pairwise guide--transcript scores do not enforce conservation of a finite guide-loaded RISC pool when they are interpreted independently as occupancies. We formulate a differentiable scalar equilibrium layer: one conservation equation with a unique positive root and exact implicit gradients. It yields a redistribution theorem, a qualified high-resource limit, an analysis of the retrieval approximation, and a conditional rank-invariance result: within one construct at one dose, rankings by fractional occupancy cannot distinguish equilibrium from independent scoring. We therefore audit the two experiments that proposition leaves open, dose and cross-context, on archival off-target data. Corrected thermodynamic affinities associate weakly with measured repression in the direction a working predictor requires, but a paired permutation test and a construct-cluster bootstrap do not establish added predictive value from the coupling: what survives their differing permutation-null baselines is \GapNet{}, a descriptive \GapNetOverSE{} of the equilibrium association's cluster standard error. The dose fits are heterogeneous and frequently violate the model-implied exponent constraint, which is superlinear rather than sublinear, so these data do not identify the competition parameter. A saturable compression of the competitor set holds both accuracy targets on held-out guide families but is not faster at the size measured. The contribution is a reusable conservation operator and the experimental information needed to test it. The code for this study is available at https://github.com/shadi97kh/One-Pool-Many-Targets.
