Structure-sensitive transformer and multi-view graph contrastive learning enhanced prediction of drug-related microbes

Background: The human microbiome plays a crucial role in regulating the efficacy and toxicity of drugs as well as in developing the drugs. Therefore, predicting the drug-related microbes is beneficial for analyzing the functional mechanisms of drugs. Recently, the graph learning based methods demons...

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Veröffentlicht in:	BMC bioinformatics Jg. 26; H. 1; S. 231 - 21
Hauptverfasser:	Xuan, Ping, Wang, Rui, Gu, Jing, Cui, Hui, Zhang, Tiangang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 26.09.2025 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Ablation Algorithms Associative learning Bioinformatics Bioinformatics and chemoinformatics in drug discovery Biomedical and Life Sciences Computational Biology - methods Computational Biology/Bioinformatics Computer Appl. in Life Sciences Data augmentation Data mining Drug development Drug discovery Drug resistance Drugs Effectiveness Embedding Graph representations Health aspects Humans Identification and classification Learnable data augmentation Life Sciences Machine Learning Methods Microarrays Microbiomes Microbiota (Symbiotic organisms) Microbiota - drug effects Microorganisms Multi-view contrastive learning Pathogens Pharmaceutical research Prediction models Proteins Semantics Staphylococcus infections Structure-sensitive transformer Topology Toxicity View-level attention mechanism China View-level attention mechanism Learnable data augmentation Structure-sensitive transformer Multi-view contrastive learning
ISSN:	1471-2105, 1471-2105
Online-Zugang:	Volltext
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