ArticleComputational and structural biotechnology journal2024
Explainable machine learning model for identifying key gut microbes and metabolites biomarkers associated with myasthenia gravis.
Article in Computational and structural biotechnology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 13 citations in OpenAlex.
- Proteomic and machine learning analysis predicts treatment response signatures in Myasthenia Gravis.Journal of translational medicine · 2026Trial
- Gut microbiota-derived metabolites as immune modulators in aging and age-related chronic inflammatory diseases.Ageing research reviews · 2026Review
- DepMicroDiff: Diffusion-Based Dependency-Aware Multimodal Imputation for Microbiome Data.Computational and structural biotechnology journal · 2026Article
- AI-Driven Personalized Nutrition: Integrating Omics, Ethics, and Digital Health.Molecular nutrition & food research · 2025Review
- Construction of the prediction model and analysis of key winning factors in world women's volleyball using gradient boosting decision tree.Scientific reports · 2025Article
- Proteomic and Machine Learning Analysis Predicts Treatment Response Signatures in Myasthenia Gravis.Research square · 2025Article
- Machine Learning in Myasthenia Gravis: A Systematic Review of Prognostic Models and AI-Assisted Clinical Assessments.Diagnostics (Basel, Switzerland) · 2025Review
- Machine learning models based on routine blood and biochemical test data for diagnosis of neurological diseases.Scientific reports · 2025Article
- Gut microbiota-derived metabolites modulate Treg/Th17 balance: novel therapeutic targets in autoimmune diseases.Frontiers in immunology · 2025Review
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diagnostic markers for myasthenia gravis (MG) are limited; thus, innovative approaches are required for supportive diagnosis and personalized care. Gut microbes are associated with MG pathogenesis; however, few studies have adopted machine learning (ML) to identify the associations among MG, gut microbiota, and metabolites. In this study, we developed an explainable ML model to predict biomarkers for MG diagnosis. We enrolled 19 MG patients and 10 non-MG individuals. Stool samples were collected and microbiome assessment was performed using 16S rRNA sequencing. Untargeted metabolic profiling was conducted to identify fecal amplicon significant variants (ASVs) and metabolites. We developed an explainable ML model in which the top ASVs and metabolites are combined to identify the best predictive performance. This model uses the SHapley Additive exPlanations method to generate both global and personalized explanations. Fecal microbe-metabolite composition differed significantly between groups. The key bacterial families were
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.