ArticleFrontiers in microbiology2023
Machine learning strategy for identifying altered gut microbiomes for diagnostic screening in myasthenia gravis.
Article in Frontiers in microbiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 12 citations in OpenAlex.
- Proteomic and machine learning analysis predicts treatment response signatures in Myasthenia Gravis.Journal of translational medicine · 2026Trial
- Radiomics and machine learning for differentiating pediatric intramedullary spinal tumors: a pilot study.European radiology experimental · 2026Article
- Gut microbial alterations and functional shifts in patients with hypertriglyceridemia: insights from a northwestern Chinese metagenomic study.International microbiology : the official journal of the Spanish Society for Microbiology · 2026Article
- Autoimmune Neuromuscular Disorders at a Molecular Crossroad: Linking Pathogenesis to Targeted Immunotherapy.International journal of molecular sciences · 2025Review
- Best practices for developing microbiome-based disease diagnostic classifiers through machine learning.Gut microbes · 2025Article
- Proteomic and Machine Learning Analysis Predicts Treatment Response Signatures in Myasthenia Gravis.Research square · 2025Article
- Explainable machine learning model for identifying key gut microbes and metabolites biomarkers associated with myasthenia gravis.Computational and structural biotechnology journal · 2024Article
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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
Myasthenia gravis (MG) is a neuromuscular junction disease with a complex pathophysiology and clinical variation for which no clear biomarker has been discovered. We hypothesized that because changes in gut microbiome composition often occur in autoimmune diseases, the gut microbiome structures of patients with MG would differ from those without, and supervised machine learning (ML) analysis strategy could be trained using data from gut microbiota for diagnostic screening of MG. Genomic DNA from the stool samples of MG and those without were collected and established a sequencing library by constructing amplicon sequence variants (ASVs) and completing taxonomic classification of each representative DNA sequence. Four ML methods, namely least absolute shrinkage and selection operator, extreme gradient boosting (XGBoost), random forest, and classification and regression trees with nested leave-one-out cross-validation were trained using ASV taxon-based data and full ASV-based data to identify key ASVs in each data set. The results revealed XGBoost to have the best predicted performance. Overlapping key features extracted when XGBoost was trained using the full ASV-based and ASV taxon-based data were identified, and 31 high-importance ASVs (HIASVs) were obtained, assigned importance scores, and ranked. The most significant difference observed was in the abundance of bacteria in the
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Registered trials
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