Application of Machine Learning in Phenotypic Prediction of Gut Microbiota

doi:10.19586/j.2095-2341.2021.0201

Current Biotechnology ›› 2023, Vol. 13 ›› Issue (5): 671-680.DOI: 10.19586/j.2095-2341.2021.0201

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Application of Machine Learning in Phenotypic Prediction of Gut Microbiota

Haitao CAO(), Jing ZHU(), Yunpeng MA, Xinghua CUI

College of Computer and Information Engineering，Xinjiang Agricultural University，Urumqi 830052，China

Received:2021-12-29 Accepted:2022-05-16 Online:2023-09-25 Published:2023-10-10
Contact: Jing ZHU

机器学习在肠道菌群宿主表型预测中的应用

曹海涛(), 朱静(), 马云鹏, 崔兴华

新疆农业大学计算机与信息工程学院，乌鲁木齐 830052

通讯作者: 朱静
作者简介:曹海涛 E-mail: 2232060551@qq.com；
基金资助:
新疆畜牧科学院畜牧研究所基础研究项目(2020BD1002-2-2-2)

Abstract

Abstract:

With the development of second-generation DNA sequencing technology， a large amount of gut microbiota data has been accumulated. The studies showed that gut microbiota were closely related to the health status of the host. Therefore how to model and analyze the complex and high-dimensional gut microbiota data has been an important challenge faced by bioinformatics at present. The rise of artificial intelligence had made it possible to process gut microbiota data and revealed the complex relationship between gut microbiota and host phenotypes. The paper summarized the present stage of gut microbiota and phenotypic correlation study among five kinds of machine learning algorithm （commonly used linear regression， support vector machine （SVM）， K-nearest neighbor， random forests and artificial neural network）， introduced five kinds of machine learning algorithms of theory and application in the related research， and to choose what kind of machine learning algorithms to predict recommendations to host phenotype. Finally， the future development of this field was prospected to provide a reference for predicting host phenotypes using machine learning based on gut microbiota data.

Key words: gut microbiota, machine learning, modeling and prediction

摘要：

随着第二代DNA测序技术的发展，研究人员积累了大量的肠道菌群数据，研究表明肠道菌群与宿主健康状况存在密切联系，因此如何对复杂、高维的肠道菌群数据进行建模分析，是当前生物信息学研究中的重要挑战。人工智能的兴起为处理肠道菌群数据，揭示肠道菌群与宿主表型之间的复杂关系提供了可能。综述了现阶段肠道菌群与宿主表型之间的相关研究，重点介绍了常用的5种机器学习算法（线性回归、支持向量机、K-近邻、随机森林、人工神经网络）的理论原理及在相关研究中的应用，对预测宿主表型的机器学习算法选择提出了建议，并对该领域的未来发展进行了展望，以期为利用机器学习对肠道菌群宿主表型预测提供参考依据。

关键词: 肠道菌群, 机器学习, 建模预测

CLC Number:

Haitao CAO, Jing ZHU, Yunpeng MA, Xinghua CUI. Application of Machine Learning in Phenotypic Prediction of Gut Microbiota[J]. Current Biotechnology, 2023, 13(5): 671-680.

曹海涛, 朱静, 马云鹏, 崔兴华. 机器学习在肠道菌群宿主表型预测中的应用[J]. 生物技术进展, 2023, 13(5): 671-680.

Figures/Tables 6

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疾病类型	样本数	负样本数	正样本数	算法类型	评价标准	预测精度
2型糖尿病	344	170	174	随机森林	AUC	0.74
				支持向量机	AUC	0.66
				弹性网	AUC	0.70
				套索	AUC	0.71
	806	423	383	逻辑回归	F1分数	0.91
				支持向量机	F1分数	0.91
				自适应提升	F1分数	0.90
				梯度提升决策树	F1分数	0.87
				K近邻	F1分数	0.86
				随机梯度下降	F1分数	0.84
				随机森林	F1分数	0.83
肝硬化	232	118	114	随机森林	AUC	0.95
				支持向量机	AUC	0.92
				弹性网	AUC	0.91
				套索	AUC	0.88
结直肠癌	121	48	73	随机森林	AUC	0.87
				支持向量机	AUC	0.81
				弹性网	AUC	0.79
				套索	AUC	0.73
肥胖	253	164	89	随机森林	AUC	0.66
				支持向量机	AUC	0.65
				弹性网	AUC	0.64
				套索	AUC	0.60
炎症性肠病	110	25	85	随机森林	AUC	0.89
				支持向量机	AUC	0.86
				弹性网	AUC	0.83
				套索	AUC	0.81
胆管炎	48	24	24	随机森林	AUC	0.74
口臭	90	45	45	深度学习	AUC	0.97
口臭	90	45	45	支持向量机	AUC	0.79
肠息肉	552	316	236	朴素贝叶斯	AUC	0.86
肠息肉	552	316	236	人工神经网络	AUC	0.87

疾病类型	样本数	负样本数	正样本数	算法类型	评价标准	预测精度
2型糖尿病	344	170	174	随机森林	AUC	0.74
				支持向量机	AUC	0.66
				弹性网	AUC	0.70
				套索	AUC	0.71
	806	423	383	逻辑回归	F1分数	0.91
				支持向量机	F1分数	0.91
				自适应提升	F1分数	0.90
				梯度提升决策树	F1分数	0.87
				K近邻	F1分数	0.86
				随机梯度下降	F1分数	0.84
				随机森林	F1分数	0.83
肝硬化	232	118	114	随机森林	AUC	0.95
				支持向量机	AUC	0.92
				弹性网	AUC	0.91
				套索	AUC	0.88
结直肠癌	121	48	73	随机森林	AUC	0.87
				支持向量机	AUC	0.81
				弹性网	AUC	0.79
				套索	AUC	0.73
肥胖	253	164	89	随机森林	AUC	0.66
				支持向量机	AUC	0.65
				弹性网	AUC	0.64
				套索	AUC	0.60
炎症性肠病	110	25	85	随机森林	AUC	0.89
				支持向量机	AUC	0.86
				弹性网	AUC	0.83
				套索	AUC	0.81
胆管炎	48	24	24	随机森林	AUC	0.74
口臭	90	45	45	深度学习	AUC	0.97
口臭	90	45	45	支持向量机	AUC	0.79
肠息肉	552	316	236	朴素贝叶斯	AUC	0.86
肠息肉	552	316	236	人工神经网络	AUC	0.87

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Application of Machine Learning in Phenotypic Prediction of Gut Microbiota

机器学习在肠道菌群宿主表型预测中的应用

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