生物拓扑语义增强的药物与微生物异质图表征学习

doi:10.16451/j.cnki.issn1003-6059.202412007

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Abstract

The interaction between microorganisms and drugs significantly impacts human health. In existing association prediction methods, the internal node information of heterogeneous graphs is not adequately modeled and the importance of different meta-path instances is overlooked. Hence, a biological topology-semantic enhanced heterogeneous graph representation learning for drug-microbe interactions(HGRL) method is proposed. High-order mixed neighborhood network embedding representations are extracted to infer microorganism-drug associations. Microorganism-drug similarity and association data are integrated to construct a weighted bidirectional heterogeneous network and a multi-view meta-path aware network. The transformer-gated graph network is combined with Bayesian Gaussian mixture weighted contrastive learning to extract topological semantics and embedding features of complex biological networks. Prediction based on adversarial negative sampling demonstrates that HGRL outperforms existing methods in microorganism-drug association prediction and is a reliable tool for inferring microorganisms associated with candidate drugs.

Key words： Heterogeneous Graph Representation Learning Complex Biological Network Multi-view Meta-Path Contrastive Learning Microbe-Drug Association Prediction

Received: 04 November 2024

ZTFLH:

TP 391

Fund:

National Natural Science Foundation of China(No.62403437), Project of Science and Technology in Henan Pro-vince(No.242102211039)

Corresponding Authors: SONG Shengli, Ph.D., professor. His research interests include intelligent computing and applications.

About author:: GUO Quanming, Master student. His research interests include bioinformatics computation and artificial intelligence.
GUO Yanbu, Ph.D., associate professor. His research interests include neural network theory and applications.
CHEN Zihao, Master student. His research interests include bioinformatics computation and artificial intelligence.
ZHU Haokun, Master student. His research interests include bioinformatics computation and artificial intelligence.

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Cite this article:

GUO Quanming,GUO Yanbu,SONG Shengli等. Biological Topology-Semantic Enhanced Heterogeneous Graph Representation Learning for Drug-Microbe Interactions[J]. Pattern Recognition and Artificial Intelligence, 2024, 37(12): 1121-1134.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202412007 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2024/V37/I12/1121

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