Enhanced metagenomic deep learning for disease prediction and consistent signature recognition by restructured microbiome 2D representations.

ABSTRACT: Metagenomic analysis has been explored for disease diagnosis and biomarker discovery. Low sample sizes, high dimensionality, and sparsity of metagenomic data challenge metagenomic investigations. Here, an unsupervised microbial embedding, grouping, and mapping algorithm (MEGMA) was developed to transform metagenomic data into individualized multichannel microbiome 2D representation by manifold learning and clustering of microbial profiles (e.g., composition, abundance, hierarchy, and taxonomy). These 2D representations enable enhanced disease prediction by established ConvNet-based AggMapNet models, outperforming the commonly used machine learning and deep learning models in metagenomic benchmark datasets. These 2D representations combined with AggMapNet explainable module robustly identif

SUBMITTER: Shen WX 

PROVIDER: S-EPMC9868677 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature