A novel microbe-drug association prediction model based on stacked autoencoder with multi-head attention mechanism

被引:10
|
作者
Fan, Liu [1 ,2 ]
Wang, Lei [2 ,3 ]
Zhu, Xianyou [1 ]
机构
[1] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421010, Peoples R China
[2] Changsha Univ, Inst Bioinformat Complex Network Big Data, Changsha 410022, Peoples R China
[3] Changsha Univ, Big Data Innovat & Entrepreneurship Educ Ctr Hunan, Changsha, Peoples R China
来源
SCIENTIFIC REPORTS | 2023年 / 13卷 / 01期
基金
中国国家自然科学基金;
关键词
PHARMACOKINETIC PROPERTIES; ANTIBACTERIAL ACTIVITY; COMPUTATIONAL MODEL; CIPROFLOXACIN; PEFLOXACIN; NETWORK; RESISTANCE;
D O I
10.1038/s41598-023-34438-8
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Microbes are intimately tied to the occurrence of various diseases that cause serious hazards to human health, and play an essential role in drug discovery, clinical application, and drug quality control. In this manuscript, we put forward a novel prediction model named MDASAE based on a stacked autoencoder (SAE) with multi-head attention mechanism to infer potential microbe-drug associations. In MDASAE, we first constructed three kinds of microbe-related and drug-related similarity matrices based on known microbe-disease-drug associations respectively. And then, we fed two kinds of microbe-related and drug-related similarity matrices respectively into the SAE to learn node attribute features, and introduced a multi-head attention mechanism into the output layer of the SAE to enhance feature extraction. Thereafter, we further adopted the remaining microbe and drug similarity matrices to derive inter-node features by using the Restart Random Walk algorithm. After that, the node attribute features and inter-node features of microbes and drugs would be fused together to predict scores of possible associations between microbes and drugs. Finally, intensive comparison experiments and case studies based on different well-known public databases under 5-fold cross-validation and 10-fold cross-validation respectively, proved that MDASAE can effectively predict the potential microbe-drug associations.
引用
收藏
页数:11
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