iMeta: Ma Yingfei's team developed a method for analyzing gut microbiota based on neural network

iMeta [IF:N/A] ① This study proposes a framework combining neural networks and random forests to identify biomarkers associated with gut microbiota and type 2 diabetes based on microbial abundance composition;② Construct a directional interaction network of biomarkers to analyze Potential drivers of related microbiota in type 2 diabetes-related changes;③ To analyze the coordinated changes of fasting blood glucose dynamic changes and biomarkers in the development of type 2 diabetes;④ This study paves the way for the use of neural network algorithms in microbiota research It opens the way and offers potential opportunities to gain insight into the role of microbes in the development of human disease and to assess individuals at risk for associated disea. Editor-in-Chief's recommendationLiu Yongxin - Chinese Academy of Sciences - MetagenomicsThis study developed a framework combining neural networks and random forests to identify 40 marker species and 90 marker genes from metagenomic datasets, and obtained higher accuracy in prediction This study provides a new way to identify disease-related biomarkers and analyze their role in disease developme. A neural network-based framework to understand the type 2 diabetes-related alteration of the human gut microbiome11002/imt2005-05, ArticleAbstract:The identification of microbial markers adequate to delineate the disease-related microbiome alterations from the complex human gut microbiota is of great intere. 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), respective. 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 < 05) in the T2D-related alteration of the human gut microbio. 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 alterati. Our study provides a new way of identifying the disease- related biomarkers and analyzing the role they may play in the development of the disea. First Authors:Shun GuoCorrespondence Authors:Qingshan Jiang,Yingfei MaAll Authors:Shun Guo,Haoran Zhang,Yunmeng Chu,Qingshan Jiang,Yingfei Ma   Disclaimer: This article only represents the author's personal opinion and has nothing to do with China Probiotics Netwo. Its originality and the text and content stated in the text have not been verified by this site, and this site does not make any guarantee or commitment to the authenticity, completeness and timeliness of this text and all or part of its content and te. Readers are only for reference and please Verify the relevant content yourse. Copyright Notice Some articles reproduced on this site are not original, and their copyright and responsibility belong to the original auth. All reprinted articles, links and pictures on this website are for the purpose of conveying more information, and the source and author are clearly indicat. Media or individuals who do not wish to be reprinted can contact us for infringing information that can provide sufficient eviden. , bio149 will be deleted within 12 hours after confirmati. Users are welcome to submit original articles to 86371366@. com, which will be published on the homepage after review, and the copyright and responsibility of the articles belong to the send.

iMeta① This study proposes a framework combining neural networks and random forests to identify biomarkers associated with gut microbiota and type 2 diabetes based on microbial abundance composition;② Construct a directional interaction network of biomarkers to analyze Potential drivers of related microbiota in type 2 diabetes-related changes;③ To analyze the coordinated changes of fasting blood glucose dynamic changes and biomarkers in the development of type 2 diabetes;④ This study paves the way for the use of neural network algorithms in microbiota research It opens the way and offers potential opportunities to gain insight into the role of microbes in the development of human disease and to assess individuals at risk for associated disea.

Editor-in-Chief's recommendationLiu Yongxin - Chinese Academy of Sciences - MetagenomicsThis study developed a framework combining neural networks and random forests to identify 40 marker species and 90 marker genes from metagenomic datasets, and obtained higher accuracy in prediction This study provides a new way to identify disease-related biomarkers and analyze their role in disease developme.

A neural network-based framework to understand the type 2 diabetes-related alteration of the human gut microbiome11002/imt2005-05, ArticleAbstract:The identification of microbial markers adequate to delineate the disease-related microbiome alterations from the complex human gut microbiota is of great intere.

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), respective.

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 < 05) in the T2D-related alteration of the human gut microbio.

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 alterati.

Our study provides a new way of identifying the disease- related biomarkers and analyzing the role they may play in the development of the disea.

First Authors:Shun GuoCorrespondence Authors:Qingshan Jiang,Yingfei MaAll Authors:Shun Guo,Haoran Zhang,Yunmeng Chu,Qingshan Jiang,Yingfei Ma

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iMeta① This study proposes a framework combining neural networks and random forests to identify biomarkers associated with gut microbiota and type 2 diabetes based on microbial abundance composition;② Construct a directional interaction network of biomarkers to analyze Potential drivers of related microbiota in type 2 diabetes-related changes;③ To analyze the coordinated changes of fasting blood glucose dynamic changes and biomarkers in the development of type 2 diabetes;④ This study paves the way for the use of neural network algorithms in microbiota research It opens the way and offers potential opportunities to gain insight into the role of microbes in the development of human disease and to assess individuals at risk for associated disea.

Editor-in-Chief's recommendationLiu Yongxin - Chinese Academy of Sciences - MetagenomicsThis study developed a framework combining neural networks and random forests to identify 40 marker species and 90 marker genes from metagenomic datasets, and obtained higher accuracy in prediction This study provides a new way to identify disease-related biomarkers and analyze their role in disease developme.

A neural network-based framework to understand the type 2 diabetes-related alteration of the human gut microbiome11002/imt2005-05, ArticleAbstract:The identification of microbial markers adequate to delineate the disease-related microbiome alterations from the complex human gut microbiota is of great intere.

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), respective.

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 < 05) in the T2D-related alteration of the human gut microbio.

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 alterati.

Our study provides a new way of identifying the disease- related biomarkers and analyzing the role they may play in the development of the disea.

First Authors:Shun GuoCorrespondence Authors:Qingshan Jiang,Yingfei MaAll Authors:Shun Guo,Haoran Zhang,Yunmeng Chu,Qingshan Jiang,Yingfei Ma

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iMeta[IF:N/A]① This study proposes a framework combining neural networks and random forests to identify biomarkers associated with gut microbiota and type 2 diabetes based on microbial abundance composition;② Constructing biomarkers To analyze the potential drivers of related microbiota in type 2 diabetes-related changes; ③ Toanalyze the coordinated changes of fasting blood glucose dynamic changes and biomarkers in the development of type 2 diabetes;The use of neural network algorithms in the study paves the way and offers potential opportunities to gain insight into the role of microbes in the development of human disease and to assess individuals at risk for associated disea.

Editor-in-Chief's recommendationLiu Yongxin - Chinese Academy of Sciences - Metagenomics This study developed a framework combining neural networks and random forests to identify 40 marker species and 90 marker genes from metagenomic datasets, and obtained higher accuracy in prediction This study provides a new way to identify disease-related biomarkers and analyze their role in disease developme.

A neural network-based framework to understand the type 2 diabetes-related alteration of the human gut microbiome11002/imt2005-05, ArticleAbstract:The identification of microbial markers adequate to delineate the disease-related microbiome alterations from the complex human gut microbiota is of great intere.

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), respective.

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 < 05) in the T2D-related alteration of the human gut microbio.

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 alterati.

Our study provides a new way of identifying the disease- related biomarkers and analyzing the role they may play in the development of the disea.

First Authors:Shun GuoCorrespondence Authors:Qingshan Jiang,Yingfei MaAll Authors:Shun Guo,Haoran Zhang,Yunmeng Chu,Qingshan Jiang,Yingfei Ma

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