AI 中文总结
SymPerturb是区分四种扰动算子与三种分析程序的虚拟扰动框架，可将症状网络结构转化为可检验的干预优先级，在22节点四模块生成网络中分析功效与蒙特卡洛估计值偏差极小，旨在生成可审计的目标假设用于实验测试。
AI 中文摘要
症状网络编码条件依赖关系，但本身无法识别因果关系或临床可执行的干预靶点。我们提出SymPerturb，这是一个虚拟扰动框架，它区分了四种基础扰动算子——虚拟敲除、虚拟敲低、边级通信阻断和节点中心通信阻断——以及三种分析程序——虚拟剂量扰动、组合扰动和序列优化。参考高斯实现嵌入在通用位置-尺度图中，具有特定于症状的目标锚点，明确表明零锚定和关联的均值-方差衰减是建模选择。七个效用结果量化了下游功效、剂量效率、广度、跨模块覆盖范围、通信阻断、组合价值和响应性；稳健性作为不确定性诊断单独报告。它们与方向对齐的候选集内加权平均值定义了虚拟扰动优先级得分（VPPS），这是一个相对排名，而非可移植的临床效用得分。在一个已知的22节点、四模块生成网络中，分析功效与10万次抽样的蒙特卡洛估计值在0.0024标准差内一致。报告的有限样本VPPS结果是使用原始的八个组成部分探索性得分生成的，因此需要在修订后的七个效用维度定义下重新生成。这些模拟在模型兼容性下提供了内部计算验证，而非因果或外部验证。SymPerturb旨在生成可审计的目标假设，用于纵向和实验测试。
英文摘要
Symptom networks encode conditional dependence but do not by themselves identify causal or clinically actionable intervention targets. We introduce SymPerturb, a virtual-perturbation framework that distinguishes four primitive perturbation operators - virtual knockout, virtual knockdown, edge-level communication blocking and node-centred communication blocking - from three analytic procedures - virtual dosage perturbation, combination perturbation and sequence optimisation. The reference Gaussian implementation is embedded in a general location-scale map with symptom-specific target anchors, making explicit that zero anchoring and linked mean-variance attenuation are modelling choices. Seven utility outcomes quantify downstream efficacy, dose efficiency, breadth, cross-module reach, communication blocking, combination value and responsiveness; robustness is reported separately as an uncertainty diagnostic. Their direction-aligned, within-candidate-set weighted mean defines the virtual perturbation priority score (VPPS), which is a relative ranking rather than a transportable clinical utility score. In a known 22-node, four-module generating network, analytical efficacy agreed with 100,000-draw Monte Carlo estimates within 0.0024 standard deviations. The reported finite-sample VPPS results were generated with the original eight-component exploratory score and therefore require regeneration under the revised seven-utility-dimension definition. These simulations provide internal computational verification under model compatibility, not causal or external validation. SymPerturb is intended to generate auditable target hypotheses for longitudinal and experimental testing.
Comments17 pages, 3 figures, and 3 tables
