Gut microbiota analysis reveals microbial signature for multi-autoimmune diseases based on machine learning model.

ABSTRACT:

Introduction

Human microbiota is a major factor contributing to the immune system, offering an opportunity to develop non-invasive methods for disease diagnosis. In some research on Autoimmune Diseases (AIDs), gut microbiota variation has been observed. However, there remains a paucity of research that explores the potential of gut microbiota as a microbial signature for the classification and diagnosis of multi-AIDs.

Methods

In this study, we analyzed 1,954 gut microbiota sequencing datasets from public databases collected from 1,043 patients with 10 AIDs to identify common or unique microbial signatures for AIDs through differential abundance testing and machine learning techniques. We evaluated five popular algorithms: Random Forest (RF), Support Vector Machine (SVM), K-N

SUBMITTER: An T 

PROVIDER: S-EPMC12507882 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature