Unveiling microbial biomarkers of ruminant methane emission through machine learning.

ABSTRACT:

Background

Enteric methane from cow burps, which results from microbial fermentation of high-fiber feed in the rumen, is a significant contributor to greenhouse gas emissions. A promising strategy to address this problem is microbiome-based precision feed, which involves identifying key microorganisms for methane production. While machine learning algorithms have shown success in associating human gut microbiome with various human diseases, there have been limited efforts to employ these algorithms to establish microbial biomarkers for methane emissions in ruminants.

Methods

In this study, we aim to identify potential methane biomarkers for methane emission from ruminants by employing regression algorithms commonly used in human microbiome studies, coupled with different fea

SUBMITTER: Peng C 

PROVIDER: S-EPMC10749206 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature