Prediction of drug-drug interaction events using graph neural networks based feature extraction.
Prediction of drug-drug interaction events using graph neural networks based feature extraction.
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DOI：
10.1038/s41598-022-19999-4
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发表时间：
2022-09-16
影响因子：
4.6
通讯作者：
Lakizadeh, Amir
中科院分区：
文献类型：
--
作者：
Al-Rabeah, Mohammad Hussain;Lakizadeh, Amir
近年来，多种药物治疗的流行度不断增加，特别是在患有多种疾病的老年人中。然而，意外的药物间相互作用（DDI）可能会导致不良反应或严重毒性，从而使患者处于危险之中。随着对多种药物治疗的需求增加，发现 DDI 变得越来越有必要。然而，在体外和体内大量药物对中进行 DDI 检测既昂贵又费力。因此，DDI鉴定是药物相关研究中最受关注的问题之一。在本文中，我们提出了 GNN-DDI，这是一种基于深度学习的方法，用于分两个阶段预测 DDI 相关事件。在第一阶段，我们从不同来源收集药物信息，然后通过形成属性异构网络来整合它们，并根据不同的药物相互作用类型和药物属性生成药物嵌入向量。在第二阶段，我们聚合表示向量，然后通过深度多模型框架执行 DDI 及其事件的预测。各种评估结果表明，所提出的方法在预测药物相互作用相关事件方面优于现有方法。实验结果表明，根据不同药物相互作用类型和属性生成药物表征是高效有效的，可以更好地展现药物的内在特征。
The prevalence of multi_drug therapies has been increasing in recent years, particularly among the elderly who are suffering from several diseases. However, unexpected Drug_Drug interaction (DDI) can cause adverse reactions or critical toxicity, which puts patients in danger. As the need for multi_drug treatment increases, it's becoming increasingly necessary to discover DDIs. Nevertheless, DDIs detection in an extensive number of drug pairs, both in-vitro and in-vivo, is costly and laborious. Therefore, DDI identification is one of the most concerns in drug-related researches. In this paper, we propose GNN-DDI, a deep learning-based method for predicting DDI-associated events in two stages. In the first stage, we collect the drugs information from different sources and then integrate them through the formation of an attributed heterogeneous network and generate a drug embedding vector based on different drug interaction types and drug attributes. In the second stage, we aggregate the representation vectors then predictions of the DDIs and their events are performed through a deep multi-model framework. Various evaluation results show that the proposed method can outperform state-of-the methods in the prediction of drug-drug interaction-associated events. The experimental results indicate that producing the drug's representations based on different drug interaction types and attributes is efficient and effective and can better show the intrinsic characteristics of a drug.
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DOI：
10.1136/amiajnl-2013-002512
发表时间：
2014-10-01
影响因子：
6.4
作者：
Cheng, Feixiong;Zhao, Zhongming
通讯作者：
Zhao, Zhongming
DOI：
10.1093/bioinformatics/bty593
发表时间：
2018-09-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Öztürk H;Özgür A;Ozkirimli E
通讯作者：
Ozkirimli E
影响因子：
3
作者：
Lee I;Nam H
通讯作者：
Nam H
影响因子：
46.9
作者：
Han K;Jeng EE;Hess GT;Morgens DW;Li A;Bassik MC
通讯作者：
Bassik MC
DOI：
10.1007/s13721-019-0215-3
发表时间：
2020-01-10
影响因子：
2.3
作者：
Rohani, Narjes;Eslahchi, Changiz;Katanforoush, Ali
通讯作者：
Katanforoush, Ali