A neural network‐based framework to understand the type 2 diabetes‐related alteration of the human gut microbiome
Abstract The identification of microbial markers adequate to delineate the disease‐related microbiome alterations from the complex human gut microbiota is of great interest. Here, we develop a framework combining neural network (NN) and random forest, resulting in 40 marker species and 90 marker gen...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-06-01
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| Series: | iMeta |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/imt2.20 |
| _version_ | 1828141392306634752 |
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| author | Shun Guo Haoran Zhang Yunmeng Chu Qingshan Jiang Yingfei Ma |
| author_facet | Shun Guo Haoran Zhang Yunmeng Chu Qingshan Jiang Yingfei Ma |
| author_sort | Shun Guo |
| collection | DOAJ |
| description | Abstract The identification of microbial markers adequate to delineate the disease‐related microbiome alterations from the complex human gut microbiota is of great interest. Here, we develop a framework combining neural network (NN) and random forest, resulting in 40 marker species and 90 marker genes identified from the metagenomic data set (185 healthy and 183 type 2 diabetes [T2D] samples), respectively. In terms of these markers, the NN model obtained higher accuracy in classifying the T2D‐related samples than other methods; the interaction network analyses identified the key species and functional modules; the regression analysis determined that fasting blood glucose is the most significant factor (p < 0.05) in the T2D‐related alteration of the human gut microbiome. We also observed that those marker species varied little across the case and control samples greatly shift in the different stages of the T2D development, suggestive of their important roles in the T2D‐related microbiome alteration. Our study provides a new way of identifying the disease‐related biomarkers and analyzing the role they may play in the development of the disease. |
| first_indexed | 2024-04-11T19:25:06Z |
| format | Article |
| id | doaj.art-2d62e7609eca44e1a43b99b9d5457bbf |
| institution | Directory Open Access Journal |
| issn | 2770-596X |
| language | English |
| last_indexed | 2024-04-11T19:25:06Z |
| publishDate | 2022-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | iMeta |
| spelling | doaj.art-2d62e7609eca44e1a43b99b9d5457bbf2022-12-22T04:07:11ZengWileyiMeta2770-596X2022-06-0112n/an/a10.1002/imt2.20A neural network‐based framework to understand the type 2 diabetes‐related alteration of the human gut microbiomeShun Guo0Haoran Zhang1Yunmeng Chu2Qingshan Jiang3Yingfei Ma4Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong ChinaShenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong ChinaShenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong ChinaShenzhen Key Lab for High Performance Data Mining, Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong ChinaShenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong ChinaAbstract The identification of microbial markers adequate to delineate the disease‐related microbiome alterations from the complex human gut microbiota is of great interest. Here, we develop a framework combining neural network (NN) and random forest, resulting in 40 marker species and 90 marker genes identified from the metagenomic data set (185 healthy and 183 type 2 diabetes [T2D] samples), respectively. In terms of these markers, the NN model obtained higher accuracy in classifying the T2D‐related samples than other methods; the interaction network analyses identified the key species and functional modules; the regression analysis determined that fasting blood glucose is the most significant factor (p < 0.05) in the T2D‐related alteration of the human gut microbiome. We also observed that those marker species varied little across the case and control samples greatly shift in the different stages of the T2D development, suggestive of their important roles in the T2D‐related microbiome alteration. Our study provides a new way of identifying the disease‐related biomarkers and analyzing the role they may play in the development of the disease.https://doi.org/10.1002/imt2.20human gut microbiotaneural networkrandom forestT2D‐related microbial markers |
| spellingShingle | Shun Guo Haoran Zhang Yunmeng Chu Qingshan Jiang Yingfei Ma A neural network‐based framework to understand the type 2 diabetes‐related alteration of the human gut microbiome iMeta human gut microbiota neural network random forest T2D‐related microbial markers |
| title | A neural network‐based framework to understand the type 2 diabetes‐related alteration of the human gut microbiome |
| title_full | A neural network‐based framework to understand the type 2 diabetes‐related alteration of the human gut microbiome |
| title_fullStr | A neural network‐based framework to understand the type 2 diabetes‐related alteration of the human gut microbiome |
| title_full_unstemmed | A neural network‐based framework to understand the type 2 diabetes‐related alteration of the human gut microbiome |
| title_short | A neural network‐based framework to understand the type 2 diabetes‐related alteration of the human gut microbiome |
| title_sort | neural network based framework to understand the type 2 diabetes related alteration of the human gut microbiome |
| topic | human gut microbiota neural network random forest T2D‐related microbial markers |
| url | https://doi.org/10.1002/imt2.20 |
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