A neural network‐based framework to understand the type 2 diabetes‐related alteration of the human gut microbiome. Issue 2 (5th May 2022)

• Record Type: Journal Article
• Authors: Guo, Shun; Zhang, Haoran; Chu, Yunmeng; Jiang, Qingshan; Ma, Yingfei
• Is Part Of: IMeta. Volume 1:Issue 2(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-05-05
• Subjects: human gut microbiota -- neural network -- random forest -- T2D‐related microbial markers; Metagenomics -- Periodicals; Bioinformatics -- Periodicals; Bioinformatics; Metagenomics; Metagenomics; Metagenome; Computational Biology; Periodicals; Periodical; 576.5
• DOI: 10.1002/imt2.20
• Languages: English
• ISSNs: 2770-596X

Abstract

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. Abstract : A framework combining neural network and random forest for identifying the type 2 diabetes (T2D)‐related biomarkers. Constructing the directed interaction networks of the biomarkers for analyzing the potential drivers of the microbial community associated with T2D. Analyzing the covary of the biomarkers with the dynamic change of fasting blood glucose in the development of T2D. Highlights: A framework combining neural network and random forest for identifying the type 2 diabetes (T2D)‐related biomarkers. Constructing the directed interaction networks of the biomarkers for analyzing the potential drivers of the microbial community associated with T2D. Analyzing the covary of the biomarkers with the dynamic change of fasting blood glucose in the development of T2D.