Interpretable machine learning algorithms reveal gut microbiome features associated with atopic dermatitis.

ABSTRACT:

Background

The "gut-skin axis" has been proposed to play an important role in the development and symptoms of atopic dermatitis. Therefore, we have constructed an interpretable machine learning framework to quantitatively screen key gut flora.

Methods

The 16S rRNA dataset, after applying the centered log-ratio transformation, was analyzed using five different machine learning models: random forest, light gradient boosting machine, extreme gradient boosting, support vector machine with radial kernel, and logistic regression. Interpretable machine learning methods, such as SHAP values, were used to identify significant features associated with atopic dermatitis.

Results

Random forest performed better than the other "tree" models in the validation partitions. The SHAP gl

SUBMITTER: Ma J 

PROVIDER: S-EPMC12078218 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature