Inferring drug-disease associations based on known protein complexes.
Inferring drug-disease associations based on known protein complexes.
复制标题
根据已知的蛋白质复合物推断药物与疾病的关联
DOI：
10.1186/1755-8794-8-s2-s2
复制
发表时间：
2015
影响因子：
2.7
通讯作者：
Gao L
中科院分区：
文献类型：
--
作者：
Yu L;Huang J;Ma Z;Zhang J;Zou Y;Gao L
推断药物-疾病关联对于揭示疾病机制以及发现可用药物的新功能或药物重新定位至关重要。以往的研究主要是基于药物-基因-疾病的关系，由于基因是通过相互作用来执行其功能的，因此忽略了许多重要的信息。为了克服这个问题，我们提出了一种新的方法，发现基于蛋白质复合物的药物-疾病关联。首先，构建了由药物、蛋白质复合物和疾病组成的集成异构网络，在该网络中，我们使用概率为药物-疾病关联分配权重。然后，从三方网络，我们得到的药物和疾病之间的间接加权关系。权重越大，相关性的可靠性越高。我们将该方法应用于精神疾病和高血压，并使用比较毒理基因组学数据库验证结果。我们的排名结果可以直接加强现有的生物医学文献，表明我们提出的方法获得更高的特异性和灵敏度。所提出的方法为药物-疾病发现提供了新的见解。我们的方法是公开的， http://1.complexdrug.sinaapp.com/Drug_Complex_Disease/Data_Download.html
Inferring drug-disease associations is critical in unveiling disease mechanisms, as well as discovering novel functions of available drugs, or drug repositioning. Previous work is primarily based on drug-gene-disease relationship, which throws away many important information since genes execute their functions through interacting others. To overcome this issue, we propose a novel methodology that discover the drug-disease association based on protein complexes. Firstly, the integrated heterogeneous network consisting of drugs, protein complexes, and disease are constructed, where we assign weights to the drug-disease association by using probability. Then, from the tripartite network, we get the indirect weighted relationships between drugs and diseases. The larger the weight, the higher the reliability of the correlation. We apply our method to mental disorders and hypertension, and validate the result by using comparative toxicogenomics database. Our ranked results can be directly reinforced by existing biomedical literature, suggesting that our proposed method obtains higher specificity and sensitivity. The proposed method offers new insight into drug-disease discovery. Our method is publicly available at http://1.complexdrug.sinaapp.com/Drug_Complex_Disease/Data_Download.html