MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing, China.
Bioinformatics. 2012 Apr 1;28(7):955-61. doi: 10.1093/bioinformatics/bts057. Epub 2012 Jan 28.
Understanding how drugs and diseases are associated in the molecular level is of critical importance to unveil disease mechanisms and treatments. Until recently, few studies attempt end to discover important gene modules shared by both drugs and diseases.
Here, we propose a novel presentation of drug-gene-disease relationship, a 'co-module', which is characterized by closely related drugs, diseases and genes. We first define a network-based gene closeness profile to relate drug to disease. Then, we develop a Bayesian partition method to identify drug-gene-disease co-modules underlying the gene closeness data. Genes share similar notable patterns with respect not only to the drugs but also the diseases within a co-module. Simulations show that our method, comCIPHER, achieves a better performance compared with a popular co-module detection method, PPA. We apply comCIPHER to a set consisting of 723 drugs, 275 diseases and 1442 genes and demonstrate that our co-module approach is able to identify new drug-disease associations and highlight their molecular basis. Disease co-morbidity emerges as well. Three co-modules are further illustrated in which new drug applications, including the anti-cancer metastasis activity of an anti-asthma drug Pranlukast, and a cardiovascular stress-testing agent Arbutamine for obesity, as well as potential side-effects, e.g. hypotension for Triamterene, are computationally identified.
The compiled version of comCIPHER can be found at http://bioinfo.au.tsinghua.edu.cn/comCIPHER/. The 86 co-modules can be downloaded from http://bioinfo.au.tsinghua.edu.cn/comCIPHER/Co_Module_Results.zip.
Supplementary data are available at Bioinformatics online.
了解药物和疾病在分子水平上的关联对于揭示疾病机制和治疗方法至关重要。直到最近，很少有研究试图发现药物和疾病之间共享的重要基因模块。
在这里，我们提出了一种新的药物-基因-疾病关系表示方法，即“共模块”，其特点是密切相关的药物、疾病和基因。我们首先定义了一种基于网络的基因接近度谱来将药物与疾病联系起来。然后，我们开发了一种贝叶斯分区方法来识别基因接近度数据下的药物-基因-疾病共模块。在一个共模块中，基因不仅与药物，而且与疾病都具有相似的显著模式。模拟表明，与一种流行的共模块检测方法 PPA 相比，我们的方法 comCIPHER 具有更好的性能。我们将 comCIPHER 应用于由 723 种药物、275 种疾病和 1442 个基因组成的一组，并证明我们的共模块方法能够识别新的药物-疾病关联，并突出其分子基础。疾病的共病也随之出现。进一步说明了三个共模块，其中包括一种抗哮喘药物普仑司特的抗癌转移活性、一种心血管应激试验剂阿脲用于肥胖症，以及潜在的副作用，如阿米洛利的低血压，这些都可以通过计算来识别。
comCIPHER 的编译版本可在 http://bioinfo.au.tsinghua.edu.cn/comCIPHER/ 找到。86 个共模块可从 http://bioinfo.au.tsinghua.edu.cn/comCIPHER/Co_Module_Results.zip 下载。
补充数据可在生物信息学在线获得。
