Machine learning strategy for identifying altered gut microbiomes for diagnostic screening in myasthenia gravis.

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, extre

SUBMITTER: Chang CC 

PROVIDER: S-EPMC10565662 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature