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Predicting Potential Microbe-disease Associations Based on Auto-encoder and Graph Convolution Network by Shanghui Lu; Yong Liang; Le Li; Rui Miao; Shuilin Liao; Yongfu Zou; Chengjun Yang; Dong Ouyang is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

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## Abstract The increasing body of research has consistently demonstrated the intricate correlation between the human microbiome and human well-being. Microbes can impact the efficacy and toxicity of drugs through various pathways, as well as influence the occurrence and metastasis of tumors. In clinical practice, it is crucial to elucidate the association between microbes and diseases. Although traditional biological experiments accurately identify this association, they are time-consuming, expensive, and susceptible to experimental conditions. Consequently, conducting extensive biological experiments to screen potential microbe-disease associations becomes challenging. The computational methods can solve the above problems well, but the previous computational methods still have the problems of low utilization of node features and the prediction accuracy needs to be improved. To address this issue, we propose the DAEGCNDF model predicting potential associations between microbes and diseases. Our model calculates four similar features for each microbe and disease. These features are fused to obtain a comprehensive feature matrix representing microbes and diseases. Our model first u

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Author

Shanghui Lu; Yong Liang; Le Li; Rui Miao; Shuilin Liao; Yongfu Zou; Chengjun Yang; Dong Ouyang

Publisher

Springer Science and Business Media LLC

Published

2023

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)