Effective Knowledge Graph Embeddings based on Multidirectional Semantics Relations for Polypharmacy Side Effects Prediction

DOI码：

10.1093/bioinformatics/btac094

所属单位：

School of Informatics, Xiamen University,

发表刊物：

Bioinformatics

刊物所在地：

United Kingdom

摘要：

Motivation: Polypharmacy is the combined use of drugs for the treatment of diseases. However, it often shows a high risk of side effects. Due to unnecessary interactions of combined drugs, the side effects of polypharmacy increase the risk of disease and even lead to death. Thus, obtaining abundant and comprehensive information on the side effects of polypharmacy is a vital task in the healthcare industry. Early traditional methods used machine learning techniques to predict side effects. However, they often make costly efforts to extract features of drugs for prediction. Later, several methods based on knowledge graphs are proposed. They are reported to outperform traditional methods. However, they still show limited performance by failing to model complex relations of side effects among drugs. Results: To resolve the above problems, we propose a novel model by further incorporating complex relations of side effects into knowledge graph embeddings. Our model can translate and transmit multidirectional semantics with fewer parameters, leading to better scalability in large-scale knowledge graphs. Experimental evaluation shows that our model outperforms state-of-the-art models in terms of the average area under the ROC and precision–recall curves. Availability and implementation: Code and data are available at: https://github.com/galaxysunwen/MSTE-master. Contact: wuqq@xmu.edu.cn or xlwang@xmu.edu.cn

第一作者：

Junfeng Yao

通讯作者：

Qingqiang Wu,Xiaoli Wang

合写作者：

Wen Sun ,Zhongquan Jian

论文类型：

Original Research

学科门类：

生物信息学 / 计算生物学 / 医疗人工智能 (AI in medicine)

文献类型：

Journal Article

卷号：

38

期号：

8

页面范围：

2315-2322

是否译文：

否

发表时间：

2022-02-15