Identification of microbe–disease signed associations via multi-scale variational graph autoencoder based on signed message propagation

Background Plenty of clinical and biomedical research has unequivocally highlighted the tremendous significance of the human microbiome in relation to human health. Identifying microbes associated with diseases is crucial for early disease diagnosis and advancing precision medicine. Results Consider...

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Veröffentlicht in:	BMC biology Jg. 22; H. 1; S. 172 - 15
Hauptverfasser:	Zhu, Huan, Hao, Hongxia, Yu, Liang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 15.08.2024 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Ablation Accuracy Algorithms Background noise Biomedical and Life Sciences Biomedical data Computational Biology - methods Disease Drugs Dysbiosis Graph representations Graph theory Graphical representations Health aspects Heterogeneity Humans Influence Information processing Life Sciences Medical research Messages Microbe-disease association Microbial metabolism Microbiomes Microbiota Microorganisms Neural networks Noise prediction Noise propagation Performance evaluation Precision medicine Propagation Regularization methods Research Article Signal processing Signed message propagation Similarity Social networks Variational graph autoencoder XGBoost China Signed message propagation Microbe-disease association Variational graph autoencoder XGBoost
ISSN:	1741-7007, 1741-7007
Online-Zugang:	Volltext
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