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Identifying disease-related microbes based on multi-scale variational graph autoencoder embedding Wasserstein distance by Huan Zhu; Hongxia Hao; Liang Yu is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

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## Background Enormous clinical and biomedical researches have demonstrated that microbes are crucial to human health. Identifying associations between microbes and diseases can not only reveal potential disease mechanisms, but also facilitate early diagnosis and promote precision medicine. Due to the data perturbation and unsatisfactory latent representation, there is a significant room for improvement. ## Results In this work, we proposed a novel framework, Multi-scale Variational Graph AutoEncoder embedding Wasserstein distance (MVGAEW) to predict disease-related microbes, which had the ability to resist data perturbation and effectively generate latent representations for both microbes and diseases from the perspective of distribution. First, we calculated multiple similarities and integrated them through similarity network confusion. Subsequently, we obtained node latent representations by improved variational graph autoencoder. Ultimately, XGBoost classifier was employed to predict potential disease-related microbes. We also introduced multi-order node embedding reconstruction to enhance the representation capacity. We also performed ablation studies to evaluate the contribut

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Author

Huan Zhu; Hongxia Hao; Liang Yu

Publisher

BioMed Central

Published

2023

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)