Yu Liang, Wang Bingbo, Ma Xiaoke, Gao Lin
School of Computer Science and Technology, Xidian University, Xi'an, 710071, People's Republic of China.
BMC Syst Biol. 2016 Dec 23;10(Suppl 4):111. doi: 10.1186/s12918-016-0364-2.
Extracting drug-disease correlations is crucial in unveiling disease mechanisms, as well as discovering new indications of available drugs, or drug repositioning. Both the interactome and the knowledge of disease-associated and drug-associated genes remain incomplete.
We present a new method to predict the associations between drugs and diseases. Our method is based on a module distance, which is originally proposed to calculate distances between modules in incomplete human interactome. We first map all the disease genes and drug genes to a combined protein interaction network. Then based on the module distance, we calculate the distances between drug gene sets and disease gene sets, and take the distances as the relationships of drug-disease pairs. We also filter possible false positive drug-disease correlations by p-value. Finally, we validate the top-100 drug-disease associations related to six drugs in the predicted results.
The overlapping between our predicted correlations with those reported in Comparative Toxicogenomics Database (CTD) and literatures, and their enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways demonstrate our approach can not only effectively identify new drug indications, but also provide new insight into drug-disease discovery.
提取药物-疾病关联对于揭示疾病机制以及发现现有药物的新适应症或药物重新定位至关重要。相互作用组以及与疾病相关和与药物相关的基因知识仍然不完整。
我们提出了一种预测药物与疾病之间关联的新方法。我们的方法基于模块距离，该距离最初是为计算不完整人类相互作用组中模块之间的距离而提出的。我们首先将所有疾病基因和药物基因映射到一个组合的蛋白质相互作用网络。然后基于模块距离，我们计算药物基因集与疾病基因集之间的距离，并将这些距离作为药物-疾病对的关系。我们还通过p值过滤可能的假阳性药物-疾病关联。最后，我们在预测结果中验证了与六种药物相关的前100个药物-疾病关联。
我们预测的关联与比较毒理基因组学数据库（CTD）和文献中报道的关联之间的重叠，以及它们丰富的京都基因与基因组百科全书（KEGG）途径表明，我们的方法不仅可以有效地识别新的药物适应症，还可以为药物-疾病发现提供新的见解。
