Multitask Dyadic Prediction and Its Application in Prediction of Adverse Drug-Drug Interaction
Multitask Dyadic Prediction and Its Application in Prediction of Adverse Drug-Drug Interaction
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DOI：
10.1609/aaai.v31i1.10718
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发表时间：
2017-02
期刊：
影响因子：
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通讯作者：
Bo Jin;Haoyu Yang;Cao Xiao;Ping Zhang;Xiaopeng Wei;Fei Wang
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文献类型：
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作者：
Bo Jin;Haoyu Yang;Cao Xiao;Ping Zhang;Xiaopeng Wei;Fei Wang
药物不良相互作用（DDI）仍然是世界各地发病率和死亡率的主要原因。在药物设计过程中识别潜在的DDI对于指导有针对性的临床药物安全性测试至关重要。尽管在IV期临床试验中进行了不良DDI的检测，但仍有大量药物在上市后意外发现了新的DDI。随着大数据时代的到来，越来越多的药物研发数据变得可用，这为挖掘可能用于药物直接投资早期预测的见解提供了宝贵的资源。近年来，人们提出了许多计算方法来预测DDI。然而，他们中的大多数人都专注于二进制预测（有或没有DDI），尽管事实上每个DDI都与不同的类型相关联。预测实际的DDI类型将有助于我们更好地理解DDI机制，并确定适当的方法来防止it.In本文中，我们制定的DDI类型预测问题作为一个多任务二元回归问题，其中每个特定的DDI类型的预测被视为一个任务。与传统的只能对DDI矩阵中的缺失项进行补元的矩阵补元方法相比，该方法可以直接对DDI进行补元，从而更容易扩展到新药的研究中。我们开发了一个有效的近似梯度方法来解决这个问题。在真实的数据集上的测试结果表明了该方法的有效性。
Adverse drug-drug interactions (DDIs) remain a leading cause of morbidity and mortality around the world. Identifying potential DDIs during the drug design process is critical in guiding targeted clinical drug safety testing. Although detection of adverse DDIs is conducted during Phase IV clinical trials, there are still a large number of new DDIs founded by accidents after the drugs were put on market. With the arrival of big data era, more and more pharmaceutical research and development data are becoming available, which provides an invaluable resource for digging insights that can potentially be leveraged in early prediction of DDIs. Many computational approaches have been proposed in recent years for DDI prediction. However, most of them focused on binary prediction (with or without DDI), despite the fact that each DDI is associated with a different type. Predicting the actual DDI type will help us better understand the DDI mechanism and identify proper ways to prevent it. In this paper, we formulate the DDI type prediction problem as a multitask dyadic regression problem, where the prediction of each specific DDI type is treated as a task. Compared with conventional matrix completion approaches which can only impute the missing entries in the DDI matrix, our approach can directly regress those dyadic relationships (DDIs) and thus can be extend to new drugs more easily. We developed an effective proximal gradient method to solve the problem. Evaluation on real world datasets is presented to demonstrate the effectiveness of the proposed approach.