A novel graph attention model for predicting frequencies of drug–side effects from multi-view data

计算机科学 图形 水准点(测量) 机器学习 注意力网络 人工智能 人工神经网络 特征(语言学) 数据挖掘 模式识别(心理学) 理论计算机科学 大地测量学 语言学 哲学 地理
作者
Haochen Zhao,Kai Zheng,Yaohang Li,Jianxin Wang
出处
期刊:Briefings in Bioinformatics [Oxford University Press]
卷期号:22 (6) 被引量:19
标识
DOI:10.1093/bib/bbab239
摘要

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.
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