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Inferring Disease-Associated Microbes Based on Multi-Data Integration and Network Consistency Projection
Plenty of microbes in our human body play a vital role in the process of cell physiology. In recent years, there is accumulating evidence indicating that microbes are closely related to many complex human diseases. In-depth investigation of disease-associated microbes can contribute to understanding...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7418576/ https://www.ncbi.nlm.nih.gov/pubmed/32850711 http://dx.doi.org/10.3389/fbioe.2020.00831 |
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author | Fan, Yongxian Chen, Meijun Zhu, Qingqi Wang, Wanru |
author_facet | Fan, Yongxian Chen, Meijun Zhu, Qingqi Wang, Wanru |
author_sort | Fan, Yongxian |
collection | PubMed |
description | Plenty of microbes in our human body play a vital role in the process of cell physiology. In recent years, there is accumulating evidence indicating that microbes are closely related to many complex human diseases. In-depth investigation of disease-associated microbes can contribute to understanding the pathogenesis of diseases and thus provide novel strategies for the treatment, diagnosis, and prevention of diseases. To date, many computational models have been proposed for predicting microbe–disease associations using available similarity networks. However, these similarity networks are not effectively fused. In this study, we proposed a novel computational model based on multi-data integration and network consistency projection for Human Microbe–Disease Associations Prediction (HMDA-Pred), which fuses multiple similarity networks by a linear network fusion method. HMDA-Pred yielded AUC values of 0.9589 and 0.9361 ± 0.0037 in the experiments of leave-one-out cross validation (LOOCV) and 5-fold cross validation (5-fold CV), respectively. Furthermore, in case studies, 10, 8, and 10 out of the top 10 predicted microbes of asthma, colon cancer, and inflammatory bowel disease were confirmed by the literatures, respectively. |
format | Online Article Text |
id | pubmed-7418576 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-74185762020-08-25 Inferring Disease-Associated Microbes Based on Multi-Data Integration and Network Consistency Projection Fan, Yongxian Chen, Meijun Zhu, Qingqi Wang, Wanru Front Bioeng Biotechnol Bioengineering and Biotechnology Plenty of microbes in our human body play a vital role in the process of cell physiology. In recent years, there is accumulating evidence indicating that microbes are closely related to many complex human diseases. In-depth investigation of disease-associated microbes can contribute to understanding the pathogenesis of diseases and thus provide novel strategies for the treatment, diagnosis, and prevention of diseases. To date, many computational models have been proposed for predicting microbe–disease associations using available similarity networks. However, these similarity networks are not effectively fused. In this study, we proposed a novel computational model based on multi-data integration and network consistency projection for Human Microbe–Disease Associations Prediction (HMDA-Pred), which fuses multiple similarity networks by a linear network fusion method. HMDA-Pred yielded AUC values of 0.9589 and 0.9361 ± 0.0037 in the experiments of leave-one-out cross validation (LOOCV) and 5-fold cross validation (5-fold CV), respectively. Furthermore, in case studies, 10, 8, and 10 out of the top 10 predicted microbes of asthma, colon cancer, and inflammatory bowel disease were confirmed by the literatures, respectively. Frontiers Media S.A. 2020-08-04 /pmc/articles/PMC7418576/ /pubmed/32850711 http://dx.doi.org/10.3389/fbioe.2020.00831 Text en Copyright © 2020 Fan, Chen, Zhu and Wang. http://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 | Bioengineering and Biotechnology Fan, Yongxian Chen, Meijun Zhu, Qingqi Wang, Wanru Inferring Disease-Associated Microbes Based on Multi-Data Integration and Network Consistency Projection |
title | Inferring Disease-Associated Microbes Based on Multi-Data Integration and Network Consistency Projection |
title_full | Inferring Disease-Associated Microbes Based on Multi-Data Integration and Network Consistency Projection |
title_fullStr | Inferring Disease-Associated Microbes Based on Multi-Data Integration and Network Consistency Projection |
title_full_unstemmed | Inferring Disease-Associated Microbes Based on Multi-Data Integration and Network Consistency Projection |
title_short | Inferring Disease-Associated Microbes Based on Multi-Data Integration and Network Consistency Projection |
title_sort | inferring disease-associated microbes based on multi-data integration and network consistency projection |
topic | Bioengineering and Biotechnology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7418576/ https://www.ncbi.nlm.nih.gov/pubmed/32850711 http://dx.doi.org/10.3389/fbioe.2020.00831 |
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