MetaBoot: a machine learning framework of taxonomical biomarker discovery for different microbial communities based on metagenomic data

doi:10.7717/peerj.993

QIBEBT-IR > 单细胞中心组群

	MetaBoot: a machine learning framework of taxonomical biomarker discovery for different microbial communities based on metagenomic data
	Wang, Xiaojun1,2,3 ; Su, Xiaoquan1,2,4 ; Cui, Xinping 1,2,5; Ning, Kang 1,2,3,4
	2015-07-07
发表期刊	PEERJ
卷号	3
摘要	As more than 90% of species in a microbial community could not be isolated and cultivated, the metagenomic methods have become one of the most important methods to analyze microbial community as a whole. With the fast accumulation of metagenomic samples and the advance of next-generation sequencing techniques, it is now possible to qualitatively and quantitatively assess all taxa (features) in a microbial community. A set of taxa with presence/absence or their different abundances could potentially be used as taxonomical biomarkers for identification of the corresponding microbial community's phenotype. Though there exist some bioinformatics methods for metagenomic biomarker discovery, current methods are not robust, accurate and fast enough at selection of non-redundant biomarkers for prediction of microbial community's phenotype. In this study, we have proposed a novel method, MetaBoot, that combines the techniques of mRMR (minimal redundancy maximal relevance) and bootstrapping, for discover of non-redundant biomarkers for microbial communities through mining of metagenomic data. MetaBoot has been tested and compared with other methods on well-designed simulated datasets considering normal and gamma distribution as well as publicly available metagenomic datasets. Results have shown that MetaBoot was robust across datasets of varied complexity and taxonomical distribution patterns and could also select discriminative biomarkers with quite high accuracy and biological consistency. Thus, MetaBoot is suitable for robustly and accurately discover taxonomical biomarkers for different microbial communities.
文章类型	Article
关键词	Biomarker Metagenomic Machine Learning Bootstrap Mrmr Taxonomical Distribution Pattern
WOS标题词	Science & Technology
DOI	10.7717/peerj.993
关键词[WOS]	HUMAN ORAL-CAVITY ; DIVERSITY ; TOOL ; CHALLENGES ; RELEVANT ; CANCER ; UNIT
收录类别	SCI
语种	英语
WOS研究方向	Science & Technology - Other Topics
WOS类目	Multidisciplinary Sciences
WOS记录号	WOS:000358689600001
引用统计	被引频次：8[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.qibebt.ac.cn/handle/337004/6524
专题	单细胞中心组群
作者单位	1.Chinese Acad Sci, Bioinformat Grp Single Cell Ctr, Shandong Key Lab Energy Genet, Qingdao, Shandong, Peoples R China 2.Chinese Acad Sci, Qingdao Inst Bioenergy & Bioproc Technol, CAS Key Lab Biofuels, Qingdao, Shandong, Peoples R China 3.Univ Chinese Acad Sci, Beijing, Peoples R China 4.Chinese Acad Sci, CUDA Res Ctr, Qingdao Inst Bioenergy & Bioproc Technol, Qingdao, Shandong, Peoples R China 5.Univ Calif Riverside, Dept Stat, Riverside, CA 92521 USA
推荐引用方式 GB/T 7714	Wang, Xiaojun,Su, Xiaoquan,Cui, Xinping,et al. MetaBoot: a machine learning framework of taxonomical biomarker discovery for different microbial communities based on metagenomic data[J]. PEERJ,2015,3.
APA	Wang, Xiaojun,Su, Xiaoquan,Cui, Xinping,&Ning, Kang.(2015).MetaBoot: a machine learning framework of taxonomical biomarker discovery for different microbial communities based on metagenomic data.PEERJ,3.
MLA	Wang, Xiaojun,et al."MetaBoot: a machine learning framework of taxonomical biomarker discovery for different microbial communities based on metagenomic data".PEERJ 3(2015).