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Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network
BACKGROUND: Microbes have dense linkages with human diseases. Balanced microorganisms protect human body against physiological disorders while unbalanced ones may cause diseases. Thus, identification of potential associations between microbes and diseases can contribute to the diagnosis and therapy...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10543759/ https://www.ncbi.nlm.nih.gov/pubmed/37789848 http://dx.doi.org/10.3389/fmicb.2023.1244527 |
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author | Peng, Lihong Huang, Liangliang Tian, Geng Wu, Yan Li, Guang Cao, Jianying Wang, Peng Li, Zejun Duan, Lian |
author_facet | Peng, Lihong Huang, Liangliang Tian, Geng Wu, Yan Li, Guang Cao, Jianying Wang, Peng Li, Zejun Duan, Lian |
author_sort | Peng, Lihong |
collection | PubMed |
description | BACKGROUND: Microbes have dense linkages with human diseases. Balanced microorganisms protect human body against physiological disorders while unbalanced ones may cause diseases. Thus, identification of potential associations between microbes and diseases can contribute to the diagnosis and therapy of various complex diseases. Biological experiments for microbe–disease association (MDA) prediction are expensive, time-consuming, and labor-intensive. METHODS: We developed a computational MDA prediction method called GPUDMDA by combining graph attention autoencoder, positive-unlabeled learning, and deep neural network. First, GPUDMDA computes disease similarity and microbe similarity matrices by integrating their functional similarity and Gaussian association profile kernel similarity, respectively. Next, it learns the feature representation of each microbe–disease pair using graph attention autoencoder based on the obtained disease similarity and microbe similarity matrices. Third, it selects a few reliable negative MDAs based on positive-unlabeled learning. Finally, it takes the learned MDA features and the selected negative MDAs as inputs and designed a deep neural network to predict potential MDAs. RESULTS: GPUDMDA was compared with four state-of-the-art MDA identification models (i.e., MNNMDA, GATMDA, LRLSHMDA, and NTSHMDA) on the HMDAD and Disbiome databases under five-fold cross validations on microbes, diseases, and microbe-disease pairs. Under the three five-fold cross validations, GPUDMDA computed the best AUCs of 0.7121, 0.9454, and 0.9501 on the HMDAD database and 0.8372, 0.8908, and 0.8948 on the Disbiome database, respectively, outperforming the other four MDA prediction methods. Asthma is the most common chronic respiratory condition and affects ~339 million people worldwide. Inflammatory bowel disease is a class of globally chronic intestinal disease widely existed in the gut and gastrointestinal tract and extraintestinal organs of patients. Particularly, inflammatory bowel disease severely affects the growth and development of children. We used the proposed GPUDMDA method and found that Enterobacter hormaechei had potential associations with both asthma and inflammatory bowel disease and need further biological experimental validation. CONCLUSION: The proposed GPUDMDA demonstrated the powerful MDA prediction ability. We anticipate that GPUDMDA helps screen the therapeutic clues for microbe-related diseases. |
format | Online Article Text |
id | pubmed-10543759 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-105437592023-10-03 Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network Peng, Lihong Huang, Liangliang Tian, Geng Wu, Yan Li, Guang Cao, Jianying Wang, Peng Li, Zejun Duan, Lian Front Microbiol Microbiology BACKGROUND: Microbes have dense linkages with human diseases. Balanced microorganisms protect human body against physiological disorders while unbalanced ones may cause diseases. Thus, identification of potential associations between microbes and diseases can contribute to the diagnosis and therapy of various complex diseases. Biological experiments for microbe–disease association (MDA) prediction are expensive, time-consuming, and labor-intensive. METHODS: We developed a computational MDA prediction method called GPUDMDA by combining graph attention autoencoder, positive-unlabeled learning, and deep neural network. First, GPUDMDA computes disease similarity and microbe similarity matrices by integrating their functional similarity and Gaussian association profile kernel similarity, respectively. Next, it learns the feature representation of each microbe–disease pair using graph attention autoencoder based on the obtained disease similarity and microbe similarity matrices. Third, it selects a few reliable negative MDAs based on positive-unlabeled learning. Finally, it takes the learned MDA features and the selected negative MDAs as inputs and designed a deep neural network to predict potential MDAs. RESULTS: GPUDMDA was compared with four state-of-the-art MDA identification models (i.e., MNNMDA, GATMDA, LRLSHMDA, and NTSHMDA) on the HMDAD and Disbiome databases under five-fold cross validations on microbes, diseases, and microbe-disease pairs. Under the three five-fold cross validations, GPUDMDA computed the best AUCs of 0.7121, 0.9454, and 0.9501 on the HMDAD database and 0.8372, 0.8908, and 0.8948 on the Disbiome database, respectively, outperforming the other four MDA prediction methods. Asthma is the most common chronic respiratory condition and affects ~339 million people worldwide. Inflammatory bowel disease is a class of globally chronic intestinal disease widely existed in the gut and gastrointestinal tract and extraintestinal organs of patients. Particularly, inflammatory bowel disease severely affects the growth and development of children. We used the proposed GPUDMDA method and found that Enterobacter hormaechei had potential associations with both asthma and inflammatory bowel disease and need further biological experimental validation. CONCLUSION: The proposed GPUDMDA demonstrated the powerful MDA prediction ability. We anticipate that GPUDMDA helps screen the therapeutic clues for microbe-related diseases. Frontiers Media S.A. 2023-09-18 /pmc/articles/PMC10543759/ /pubmed/37789848 http://dx.doi.org/10.3389/fmicb.2023.1244527 Text en Copyright © 2023 Peng, Huang, Tian, Wu, Li, Cao, Wang, Li and Duan. 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 | Microbiology Peng, Lihong Huang, Liangliang Tian, Geng Wu, Yan Li, Guang Cao, Jianying Wang, Peng Li, Zejun Duan, Lian Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network |
title | Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network |
title_full | Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network |
title_fullStr | Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network |
title_full_unstemmed | Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network |
title_short | Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network |
title_sort | predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network |
topic | Microbiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10543759/ https://www.ncbi.nlm.nih.gov/pubmed/37789848 http://dx.doi.org/10.3389/fmicb.2023.1244527 |
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