School of Computer Science and Engineering, Central South University, Changsha 410083, China.
Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China.
Brief Bioinform. 2021 Nov 5;22(6). doi: 10.1093/bib/bbab239.
Identifying the frequencies of the drug-side effects is a very important issue in pharmacological studies and drug risk-benefit. However, designing clinical trials to determine the frequencies is usually time consuming and expensive, and most existing methods can only predict the drug-side effect existence or associations, not their frequencies. Inspired by the recent progress of graph neural networks in the recommended system, we develop a novel prediction model for drug-side effect frequencies, using a graph attention network to integrate three different types of features, including the similarity information, known drug-side effect frequency information and word embeddings. In comparison, the few available studies focusing on frequency prediction use only the known drug-side effect frequency scores. One novel approach used in this work first decomposes the feature types in drug-side effect graph to extract different view representation vectors based on three different type features, and then recombines these latent view vectors automatically to obtain unified embeddings for prediction. The proposed method demonstrates high effectiveness in 10-fold cross-validation. The computational results show that the proposed method achieves the best performance in the benchmark dataset, outperforming the state-of-the-art matrix decomposition model. In addition, some ablation experiments and visual analyses are also supplied to illustrate the usefulness of our method for the prediction of the drug-side effect frequencies. The codes of MGPred are available at https://github.com/zhc940702/MGPred and https://zenodo.org/record/4449613.
确定药物副作用的频率在药理学研究和药物风险效益中是一个非常重要的问题。然而，设计临床试验来确定这些频率通常既耗时又昂贵，并且大多数现有的方法只能预测药物副作用的存在或关联，而不能预测其频率。受最近图神经网络在推荐系统中取得的进展的启发，我们使用图注意力网络开发了一种用于药物副作用频率预测的新模型，该模型集成了三种不同类型的特征，包括相似性信息、已知药物副作用频率信息和词向量。相比之下，少数专注于频率预测的可用研究仅使用已知药物副作用频率评分。这项工作中使用的一种新颖方法首先将药物副作用图中的特征类型分解，根据三种不同类型的特征提取不同视图的表示向量，然后自动重新组合这些潜在的视图向量，以获得用于预测的统一嵌入。该方法在 10 折交叉验证中表现出很高的有效性。计算结果表明，在所研究的基准数据集上，该方法在性能上优于最先进的矩阵分解模型。此外，还提供了一些消融实验和可视化分析，以说明我们的方法对于药物副作用频率预测的有用性。MGPred 的代码可在 https://github.com/zhc940702/MGPred 和 https://zenodo.org/record/4449613 上获得。
