传统流行病学被称为“黑盒子”流行病学。基于高通量组学技术的系统流行病学为打开黑盒子阐明危险因子致病通路/网络提供了机遇，在人群中比较组间(疾病VS.健康、干预VS.对照等)网络差异以推断危险因子致病通路及效应是其核心所在。然而，目前尚缺乏系统流行病学网络比较方法。本项目针对网络节点、边、方向共变特征，提出“结构拆分→数理整合”创新策略：采用节点+边、边×加权节点、节点与边整合得分策略构建3类无向网络/通路差异比较统计量；通过定义网络内上下游节点间调控权重，构建融节点、边和方向差异为一体的有向网络/有向通路比较统计量。利用统计模拟评价其稳定性、检验效能和优劣性，理论证明获得其参数统计量，实证分析心血管病、食管癌的系统流行病学网络数据验证其实用性。以期构建系统流行病学网络差异比较的统计方法体系及其R软件包，为推断致病通路，设计药物靶点，预测疾病发生、预后，制定及评估干预策略提供新方法。

Traditional epidemiology is usually referred to as “black box" epidemiology. To open the ‘black box‘ and illustrate the disease related biological pathways/networks, systems epidemiology based on high-throughput-omics technologies has created providing new insights. a The comparison of biological networks between groups (e.g. case vs. control, intervention vs. control) in population level is considered as the core of systems epidemiology, aming at identifying the disease related biological pathways and their effect on contributing to disease. However, effective methodology of network comparison is still lacking in systems epidemiology. In this project, targeting at the feature of the nodes, edges and direction changed simultaneously in the networks, an innovative and adaptive strategy of “Structural dissection → Statistical integration” is proposed. We will construct 3 statistics for detecting the undirected network/pathway difference, with “nodes + edges”, “edges ×weighted nodes” and “integrated score of nodes and edges”. Furthermore, we will construct a statistic integrating the nodes, edges and direction to detect the difference of the directed network/pathway that in principle capture the nodes and edges change, while accounting for the topology structure through putting on more weights on the difference of nodes locating on relatively more important role. Then, statistical simulations will be conducted to examine the stability and power of these 4 statistics, and acquire estimates of parameters by theoretical proof. Network data of cardiovascular disease and esophageal carcinoma will be analyzed to evaluate the practicability of the above proposed statistics. We hope our statistical methodologies of detecting networks difference in systems epidemiology will be constructed through this project, and it will help infer disease pathway, design drug targets, predict prognosis of diseases, as well as develop and evaluate intervention strategies.