Evaluation of knowledge graph embedding approaches for drug-drug interaction prediction in realistic settings
Evaluation of knowledge graph embedding approaches for drug-drug interaction prediction in realistic settings
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DOI：
10.1186/s12859-019-3284-5
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发表时间：
2019-12-18
影响因子：
3
通讯作者：
Dumontier, Michel
中科院分区：
文献类型：
--
作者：
Celebi, Remzi;Uyar, Huseyin;Dumontier, Michel
背景资料：目前识别药物间相互作用（DDI）的方法，包括药物开发期间的安全性研究和批准后的上市后监测，为识别潜在的安全问题提供了重要机会，但无法提供所有可能的DDI的完整集合。因此，药物发现研究人员和医疗保健专业人员可能没有充分意识到潜在的危险DDI。预测潜在的药物相互作用有助于减少意外的药物相互作用和药物开发成本，并优化药物设计过程。预测DDI的方法有报告高准确性的趋势，但由于网络/配对数据引起的系统性偏倚，对转化研究的影响仍然很小。在这项工作中，我们的目标是提出现实的评估设置，使用知识图嵌入来预测DDI。我们提出了一个简单的不相交的交叉验证计划，以评估药物相互作用的预测的情况下，药物没有已知的DDIs.Results：我们设计了不同的评估设置，以准确地评估预测DDIs的性能。正如预期的那样，不相交交叉验证的设置产生了较低的性能评分，但仍然能够很好地预测药物相互作用。我们在DrugBank知识图上应用了Logistic回归，Naive Bayes和Random Forest，并使用RDF 2 Vec，TransE和TransD进行了10倍传统交叉验证。使用Skip-Gram的RDF 2 Vec通常优于其他嵌入方法。我们还在各种药物知识图上测试了RDF 2 Vec，如DrugBank，PharmGKB和KEGG，以预测未知的药物相互作用。当一个集成的知识图，包括这三个dataset.Conclusion：我们表明，知识嵌入是强大的预测和目前的国家的最先进的方法推断新的DDI相比，性能没有显着提高。我们通过引入药物和成对不相交检验类来解决评估偏倚。虽然药物和成对不相交的性能评分似乎较低，但可以认为结果在预测药物相互作用信息有限的情况下是现实的。
Background: Current approaches to identifying drug-drug interactions (DDIs), include safety studies during drug development and post-marketing surveillance after approval, offer important opportunities to identify potential safety issues, but are unable to provide complete set of all possible DDIs. Thus, the drug discovery researchers and healthcare professionals might not be fully aware of potentially dangerous DDIs. Predicting potential drug-drug interaction helps reduce unanticipated drug interactions and drug development costs and optimizes the drug design process. Methods for prediction of DDIs have the tendency to report high accuracy but still have little impact on translational research due to systematic biases induced by networked/paired data. In this work, we aimed to present realistic evaluation settings to predict DDIs using knowledge graph embeddings. We propose a simple disjoint cross-validation scheme to evaluate drug-drug interaction predictions for the scenarios where the drugs have no known DDIs.Results: We designed different evaluation settings to accurately assess the performance for predicting DDIs. The settings for disjoint cross-validation produced lower performance scores, as expected, but still were good at predicting the drug interactions. We have applied Logistic Regression, Naive Bayes and Random Forest on DrugBank knowledge graph with the 10-fold traditional cross validation using RDF2Vec, TransE and TransD. RDF2Vec with Skip-Gram generally surpasses other embedding methods. We also tested RDF2Vec on various drug knowledge graphs such as DrugBank, PharmGKB and KEGG to predict unknown drug-drug interactions. The performance was not enhanced significantly when an integrated knowledge graph including these three datasets was used.Conclusion: We showed that the knowledge embeddings are powerful predictors and comparable to current state-of-the-art methods for inferring new DDIs. We addressed the evaluation biases by introducing drug-wise and pairwise disjoint test classes. Although the performance scores for drug-wise and pairwise disjoint seem to be low, the results can be considered to be realistic in predicting the interactions for drugs with limited interaction information.