Prediction of drugs having opposite effects on disease genes in a directed network.
Prediction of drugs having opposite effects on disease genes in a directed network.
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DOI：
10.1186/s12918-015-0243-2
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发表时间：
2016-01-11
影响因子：
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通讯作者：
Lee D
中科院分区：
文献类型：
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作者：
Yu H;Choo S;Park J;Jung J;Kang Y;Lee D
开发已批准药物的新用途，称为药物重新定位，可以减少传统药物开发的成本和时间。基于网络的方法在这一领域取得了可喜的成果。然而，尽管在药物-靶点相互作用和分子通路中存在各种类型的相互作用，如激活或抑制，但大多数以前基于网络的研究忽略了这一信息。我们开发了一种新的计算方法，预测药物对疾病基因有相反的影响（PDOD），用于识别药物对疾病基因的改变状态有相反的影响。PDOD利用具有“效应类型”的药物-药物靶标相互作用、具有“效应类型”和“效应方向”的整合定向分子网络以及疾病患者中具有调节状态的疾病基因。有了这些信息，我们提出了一个评分函数，发现药物可能恢复疾病基因的改变状态，使用从药物到疾病的路径，通过药物-药物靶点相互作用，从药物靶点到疾病基因的分子通路中的最短路径，以及疾病基因-疾病关联。我们收集了药物-药物靶点相互作用、分子通路和疾病基因及其在疾病中的调节状态。PDOD应用于898种已知药物-药物靶点相互作用的药物和9种疾病。我们比较了PDOD预测已知治疗药物与疾病相关性的性能与以前的方法。PDOD优于其他以前的方法，不利用分子网络中的方向信息。此外，我们还提供了一个简单的Web服务，研究人员可以提交感兴趣的基因及其改变的状态，并将在http://gto.kaist.ac.kr/pdod/index.php/main上获得似乎对输入基因的改变状态具有相反影响的药物。我们的研究结果表明，基于网络的方法中的“效果类型”和“效果方向”信息可以用于识别对疾病具有相反效果的药物。我们的研究可以为基于网络的药物重新定位领域提供新的见解。本文的在线版本（doi：10.1186/s12918-015-0243-2）包含补充材料，可供授权用户使用。
Developing novel uses of approved drugs, called drug repositioning, can reduce costs and times in traditional drug development. Network-based approaches have presented promising results in this field. However, even though various types of interactions such as activation or inhibition exist in drug-target interactions and molecular pathways, most of previous network-based studies disregarded this information. We developed a novel computational method, Prediction of Drugs having Opposite effects on Disease genes (PDOD), for identifying drugs having opposite effects on altered states of disease genes. PDOD utilized drug-drug target interactions with ‘effect type’, an integrated directed molecular network with ‘effect type’ and ‘effect direction’, and disease genes with regulated states in disease patients. With this information, we proposed a scoring function to discover drugs likely to restore altered states of disease genes using the path from a drug to a disease through the drug-drug target interactions, shortest paths from drug targets to disease genes in molecular pathways, and disease gene-disease associations. We collected drug-drug target interactions, molecular pathways, and disease genes with their regulated states in the diseases. PDOD is applied to 898 drugs with known drug-drug target interactions and nine diseases. We compared performance of PDOD for predicting known therapeutic drug-disease associations with the previous methods. PDOD outperformed other previous approaches which do not exploit directional information in molecular network. In addition, we provide a simple web service that researchers can submit genes of interest with their altered states and will obtain drugs seeming to have opposite effects on altered states of input genes at http://gto.kaist.ac.kr/pdod/index.php/main. Our results showed that ‘effect type’ and ‘effect direction’ information in the network based approaches can be utilized to identify drugs having opposite effects on diseases. Our study can offer a novel insight into the field of network-based drug repositioning. The online version of this article (doi:10.1186/s12918-015-0243-2) contains supplementary material, which is available to authorized users.