ArticleiMeta2022
A neural network-based framework to understand the type 2 diabetes-related alteration of the human gut microbiome.
Article in iMeta, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- AI-empowered human microbiome research.Gut · 2026Review
- MGM as a Large-Scale Pretrained Foundation Model for Microbiome Analyses in Diverse Contexts.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Intratumoral Collinsella aerofaciens exhibits antitumor activity in endometrial carcinoma through activation of the p53 signaling pathway.Journal of translational medicine · 2025Article
- From deep learning discovery to clinical validation: a new composite marker predicts mortality in type 2 diabetes.Cardiovascular diabetology. Endocrinology reports · 2025Article
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- Using gut microbiome metagenomic hypervariable features for diabetes screening and typing through supervised machine learning.Microbial genomics · 2025Article
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- Deep learning methods in metagenomics: a review.Microbial genomics · 2024Review
- Decoding cardiovascular risks: analyzing type 2 diabetes mellitus and ASCVD gene expression.Frontiers in endocrinology · 2024Article
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- Review
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Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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 (
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