Figure 2a Distribution of average network-based proximity (<i>P</i>_<em>QAB</em>) between a query disease module (Q) and drug modules (A and B).

This dataset contains the network-based proximity between 1,488 drug modules and six disease modules. This is a 6637968 rows by 5 columns matrix. You can load this data by R using "load" function. Run "Scripto_for_Figure2a.R" for making figure 2a in the manuscript.<br><br>Drug_A: KEGG drug ID of one of the drug pairs (Drug A)Drug_B: KEGG drug ID of the other of the drug pairs (Drug B)<br>variable: disease namevalue: the network-based proximity between Drug A and Drug B<br>IF: if the drug combination is effective on the disease. "ALL" means No, "posi" means yes本数据集包含1488个药物模块与6个疾病模块之间的基于网络的邻近度（network-based proximity）。该数据集为6637968行×5列的矩阵。您可通过R语言的`load`函数加载该数据，运行`Scripto_for_Figure2a.R`即可复现论文中的图2a。 Drug_A：药物对中药物A的KEGG药物标识符（KEGG drug ID） Drug_B：药物对中药物B的KEGG药物标识符（KEGG drug ID） variable：疾病名称 value：药物A与药物B之间的基于网络的邻近度 IF：用于标注该药物联合疗法对对应疾病是否有效，其中"ALL"代表无效，"posi"代表有效

搜集汇总

背景与挑战

背景概述

该数据集包含1,488个药物模块与6个疾病模块之间的网络邻近性数据，总计6,637,968行和5列，用于分析药物组合对疾病的潜在有效性。数据集附有R脚本以生成相关图表，并支持网络医学和协同药物组合研究。