Interpretable machine learning decodes soil microbiome's response to drought stress.

ABSTRACT:

Background

Extreme weather events induced by climate change, particularly droughts, have detrimental consequences for crop yields and food security. Concurrently, these conditions provoke substantial changes in the soil bacterial microbiota and affect plant health. Early recognition of soil affected by drought enables farmers to implement appropriate agricultural management practices. In this context, interpretable machine learning holds immense potential for drought stress classification of soil based on marker taxa.

Results

This study demonstrates that the 16S rRNA-based metagenomic approach of Differential Abundance Analysis methods and machine learning-based Shapley Additive Explanation values provide similar information. They exhibit their potential as complementary app

SUBMITTER: Hagen M 

PROVIDER: S-EPMC11138018 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature