基于机器学习的肠道菌群数据建模与分析研究综述

Review of gut microbiome analysis prediction models and algorithms

摘要:

人体肠道菌群与人类的健康和疾病存在密切关系，对肠道菌群的宏基因组数据进行建模和分析，在疾病预测及诊断相关领域科学研究和社会应用方面均具有重要意义。本文从大数据分析和机器学习的角度，对人体肠道菌群数据的建模、分析和预测算法的原理、过程以及典型研究应用实例进行综述，以期推动肠道菌群分析相关研究发展以及探索结合机器学习算法进行肠道菌群分析的有效方式，同时也为开发基于肠道菌群数据的新型诊疗手段提供借鉴，推动我国精准医疗事业发展。

Abstract:

Human gut microbiota is closely related to human health and diseases, so that the modeling and analysis of its metagenomic data is of great significance for scientific research and social application in the field of disease prediction and diagnosis. In this paper, we comprehensively assessed the tools of human gut microbiome data analysis, the principles and processes of prediction algorithms, as well as some typical application cases from the perspective of big data analysis and machine learning. It aims to promote the development of analysis technology for gut microbiome and explore effective approaches for gut microbiome analysis combined with machine learning algorithms. Furthermore, it can also provide reference for the development of new diagnosis and treatment methods based on gut microbiome data.