Prediction of adverse drug reactions by a network based external link prediction method
Prediction of adverse drug reactions by a network based external link prediction method
复制标题
基于网络的外部链接预测方法对药品不良反应的预测
DOI：
10.1039/c3ay41290c
复制
发表时间：
2013-01-01
影响因子：
3.1
通讯作者：
Li, Menglong
中科院分区：
文献类型：
--
作者：
Lin, Jiao;Kuang, Qifan;Li, Menglong
检测药物不良反应（ADR）是药物研发和上市后应用的一大挑战。由于成本低、性能高，计算方法被用来预测药物的未知不良反应。在本研究中，开发了一种基于网络的方法，其中引入二分网络来表示 ADR 和药物之间的关联。药物的潜在 ADR 可以通过其在二分网络中的邻域简单地推断出来。我们的方法应用于从 FAERS、SIDER 和这两个数据库的交集（黄金标准数据）编译的三个数据集。取得了令人鼓舞的结果，曲线下面积（AUC）值分别为 0.93、0.94 和 0.83。为了进一步评估我们方法的性能，在金标准数据上与内部链接预测方法和逻辑回归方法进行了比较。我们的方法的 AUC 值为 0.83，而内部链接预测方法和逻辑回归方法的 AUC 值为 0.75。结果表明，仅使用药物-ADR 网络的拓扑特征来预测未知的药物-ADR 关联是可行的。
Detecting adverse drug reaction (ADR) is a big challenge to drug development and post-marketing applications. Owing to the low costs and high performance, computational methods are used to predict unknown adverse reactions of drugs. In the present study, a network based method is developed, in which a bipartite network is introduced to represent associations between ADRs and drugs. The potential ADRs of a drug could be simply inferred by its neighbourhood in the bipartite network. Our method was applied on three datasets compiled from FAERS, SIDER and intersection of these two databases (gold standard data). Encouraging results were achieved, area under curve (AUC) values were 0.93, 0.94 and 0.83, respectively. To further evaluate the performance of our method, comparisons were made with internal link prediction method and logistic regression method on the gold standard data. Our method achieved an AUC value of 0.83, while the AUC values were 0.75 for both internal link prediction method and logistic regression method. The results show that it is feasible to predict unknown drug-ADR associations using only topology features of the drug-ADR network.