MetaBoot: a machine learning framework of taxonomical biomarker discovery for different microbial communities based on metagenomic data
文献类型：期刊论文
| 作者 | Wang, Xiaojun1,2,3 ; Su, Xiaoquan1,2,4; Cui, Xinping1,2,5; Ning, Kang1,2,3,4
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| 刊名 | PEERJ
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| 出版日期 | 2015-07-07 |
| 卷号 | 3 |
| 关键词 | Biomarker Metagenomic Machine learning Bootstrap mRMR Taxonomical distribution pattern |
| 英文摘要 | 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. |
| WOS标题词 | Science & Technology |
| 类目[WOS] | Multidisciplinary Sciences |
| 研究领域[WOS] | Science & Technology - Other Topics |
| 关键词[WOS] | HUMAN ORAL-CAVITY ; DIVERSITY ; TOOL ; CHALLENGES ; RELEVANT ; CANCER ; UNIT |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000358689600001 |
| 公开日期 | 2015-12-24 |
| 源URL | [http://ir.qibebt.ac.cn/handle/337004/6524]  |
| 专题 | 青岛生物能源与过程研究所_单细胞中心 |
| 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). |
入库方式： OAI收割
来源：青岛生物能源与过程研究所
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