Application of machine learning to the analysis of next-generation sequencing data of intestinal flora

VernacularTitle:机器学习在肠道菌群二代测序数据分析中的应用
Author: Jiaxin WANG ¹ ; Miao SUN ¹ ; Qi ZHOU ¹ ; Jiancheng XU ¹
Author Information

1. 吉林大学第一医院检验科，长春130021
Publication Type:Journal Article
Keywords: Metagenome; Next-generation sequencing; Machine learning; Intestinal flora; Biomarker
From: Chinese Journal of Laboratory Medicine 2025;48(2):186-191
CountryChina
Language:Chinese
Abstract: Metagenomic next-generation sequencing, as an unbiased detection technology, demonstrates higher diagnostic efficacy than traditional methods. Gut microorganisms are important flora for safeguarding health and have become a hot research topic. Modeling and analyzing the genomic data of intestinal flora using machine learning is very important in disease prediction and diagnosis. This paper briefly introduces the characteristics of metagenomic next-generation sequencing, key algorithms and evaluation indexes of machine learning, outlines the main steps of combining machine learning with metagenomic next-generation sequencing, and summarizes the application of the combination of machine learning and metagenomic next-generation sequencing technology in the study of intestinal flora, which will provide a more accurate method for diagnosis and prediction of the related diseases, and give more ideas for the future research and clinical practice.