发表机构
The University of Hong Kong; ElohaX; University of Cambridge; CASBS, Stanford University（香港大学; ElohaX; 剑桥大学; 斯坦福大学行为科学高等研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出熵引导的反向因果框架，整合知识图谱与多阶段验证，从药物反向追踪并优先排序上游瓶颈基因，在阿尔茨海默病中发现EGFR等共享候选基因，连接疾病机制与干预选择。
AI 中文摘要
识别连接多个疾病过程与治疗干预的上游调控因子是阿尔茨海默病药物发现的核心目标。我们提出一个熵引导的反向因果框架，使候选瓶颈基因成为疾病机制、通路、分子靶点和药物之间的组织纽带。该方法整合了五个阶段：一个阿尔茨海默病特异性知识图谱，辅以语言模型辅助和专家评审；从药物到候选基因的反向追踪；熵引导的优先级排序；正向传播到药物及互补组合；以及带证据反馈的分阶段验证。其新颖之处在于在一个动态、双向的发现架构中整合了上游瓶颈识别、熵引导优先级排序和迭代治疗选择。我们在使用DeepDrug2和MSigDB通路注释的计算可行性研究中展示了其分子追踪和基因优先级排序组件。通过一个包含11,300个分子和药物节点的网络追踪氨氯地平、吲达帕胺和阿托伐他汀，识别出通往九个基因的46条路径。EGFR是首要候选基因，由来自所有三种药物的26条路径支持；MME和MAF紧随其后。这些结果表明药理学起点如何识别具有明确分子连接的共享候选基因。该框架的科学意义在于将趋同的疾病机制与系统化的干预选择联系起来，以保持认知和独立性作为转化目标。
英文摘要
Identifying upstream regulators that connect several disease processes to therapeutic interventions is a central objective in Alzheimer's disease drug discovery. We propose an entropy-guided reverse-causal framework that makes candidate bottleneck genes the organizing link between disease mechanisms, pathways, molecular targets and drugs. The methodology integrates five stages: an Alzheimer's-specific knowledge graph with language-model assistance and expert review; reverse tracing from drugs to candidate genes; entropy-guided prioritization; forward propagation to drugs and complementary combinations; and staged validation with evidence feedback. The novelty lies in integrating upstream bottleneck identification, entropy-guided prioritization and iterative therapeutic selection within a dynamic, bidirectional discovery architecture. We demonstrate its molecular tracing and gene-prioritization components in a computational feasibility study using DeepDrug2 and MSigDB pathway annotations. Tracing amlodipine, indapamide and atorvastatin through a network of 11,300 molecular and drug nodes identifies 46 routes to nine genes. EGFR is the leading candidate, supported by 26 routes from all three drugs; MME and MAF rank next. These results show how pharmacological starting points can identify shared candidate genes with defined molecular connections. The framework's scientific significance lies in connecting convergent disease mechanisms to systematic intervention selection, with preservation of cognition and independence as the translational objective.
