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Identifying microbe-disease association based on graph convolutional attention network: Case study of liver cirrhosis and epilepsy

The interactions between the microbiota and the human host can affect the physiological functions of organs (such as the brain, liver, gut, etc.). Accumulating investigations indicate that the imbalance of microbial community is closely related to the occurrence and development of diseases. Thus, th...

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Autores principales: Shi, Kai, Li, Lin, Wang, Zhengfeng, Chen, Huazhou, Chen, Zilin, Fang, Shuanfeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9892757/
https://www.ncbi.nlm.nih.gov/pubmed/36741060
http://dx.doi.org/10.3389/fnins.2022.1124315
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author Shi, Kai
Li, Lin
Wang, Zhengfeng
Chen, Huazhou
Chen, Zilin
Fang, Shuanfeng
author_facet Shi, Kai
Li, Lin
Wang, Zhengfeng
Chen, Huazhou
Chen, Zilin
Fang, Shuanfeng
author_sort Shi, Kai
collection PubMed
description The interactions between the microbiota and the human host can affect the physiological functions of organs (such as the brain, liver, gut, etc.). Accumulating investigations indicate that the imbalance of microbial community is closely related to the occurrence and development of diseases. Thus, the identification of potential links between microbes and diseases can provide insight into the pathogenesis of diseases. In this study, we propose a deep learning framework (MDAGCAN) based on graph convolutional attention network to identify potential microbe-disease associations. In MDAGCAN, we first construct a heterogeneous network consisting of the known microbe-disease associations and multi-similarity fusion networks of microbes and diseases. Then, the node embeddings considering the neighbor information of the heterogeneous network are learned by applying graph convolutional layers and graph attention layers. Finally, a bilinear decoder using node embedding representations reconstructs the unknown microbe-disease association. Experiments show that our method achieves reliable performance with average AUCs of 0.9778 and 0.9454 ± 0.0038 in the frameworks of Leave-one-out cross validation (LOOCV) and 5-fold cross validation (5-fold CV), respectively. Furthermore, we apply MDAGCAN to predict latent microbes for two high-risk human diseases, i.e., liver cirrhosis and epilepsy, and results illustrate that 16 and 17 out of the top 20 predicted microbes are verified by published literatures, respectively. In conclusion, our method displays effective and reliable prediction performance and can be expected to predict unknown microbe-disease associations facilitating disease diagnosis and prevention.
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spelling pubmed-98927572023-02-03 Identifying microbe-disease association based on graph convolutional attention network: Case study of liver cirrhosis and epilepsy Shi, Kai Li, Lin Wang, Zhengfeng Chen, Huazhou Chen, Zilin Fang, Shuanfeng Front Neurosci Neuroscience The interactions between the microbiota and the human host can affect the physiological functions of organs (such as the brain, liver, gut, etc.). Accumulating investigations indicate that the imbalance of microbial community is closely related to the occurrence and development of diseases. Thus, the identification of potential links between microbes and diseases can provide insight into the pathogenesis of diseases. In this study, we propose a deep learning framework (MDAGCAN) based on graph convolutional attention network to identify potential microbe-disease associations. In MDAGCAN, we first construct a heterogeneous network consisting of the known microbe-disease associations and multi-similarity fusion networks of microbes and diseases. Then, the node embeddings considering the neighbor information of the heterogeneous network are learned by applying graph convolutional layers and graph attention layers. Finally, a bilinear decoder using node embedding representations reconstructs the unknown microbe-disease association. Experiments show that our method achieves reliable performance with average AUCs of 0.9778 and 0.9454 ± 0.0038 in the frameworks of Leave-one-out cross validation (LOOCV) and 5-fold cross validation (5-fold CV), respectively. Furthermore, we apply MDAGCAN to predict latent microbes for two high-risk human diseases, i.e., liver cirrhosis and epilepsy, and results illustrate that 16 and 17 out of the top 20 predicted microbes are verified by published literatures, respectively. In conclusion, our method displays effective and reliable prediction performance and can be expected to predict unknown microbe-disease associations facilitating disease diagnosis and prevention. Frontiers Media S.A. 2023-01-19 /pmc/articles/PMC9892757/ /pubmed/36741060 http://dx.doi.org/10.3389/fnins.2022.1124315 Text en Copyright © 2023 Shi, Li, Wang, Chen, Chen and Fang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Shi, Kai
Li, Lin
Wang, Zhengfeng
Chen, Huazhou
Chen, Zilin
Fang, Shuanfeng
Identifying microbe-disease association based on graph convolutional attention network: Case study of liver cirrhosis and epilepsy
title Identifying microbe-disease association based on graph convolutional attention network: Case study of liver cirrhosis and epilepsy
title_full Identifying microbe-disease association based on graph convolutional attention network: Case study of liver cirrhosis and epilepsy
title_fullStr Identifying microbe-disease association based on graph convolutional attention network: Case study of liver cirrhosis and epilepsy
title_full_unstemmed Identifying microbe-disease association based on graph convolutional attention network: Case study of liver cirrhosis and epilepsy
title_short Identifying microbe-disease association based on graph convolutional attention network: Case study of liver cirrhosis and epilepsy
title_sort identifying microbe-disease association based on graph convolutional attention network: case study of liver cirrhosis and epilepsy
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9892757/
https://www.ncbi.nlm.nih.gov/pubmed/36741060
http://dx.doi.org/10.3389/fnins.2022.1124315
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