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Nat Methods. 2011 Jul 17;8(9):761-3. doi: 10.1038/nmeth.1650.
2
Commensal bacteria play a role in mating preference of Drosophila melanogaster.共生细菌在果蝇的交配偏好中起作用。
Proc Natl Acad Sci U S A. 2010 Nov 16;107(46):20051-6. doi: 10.1073/pnas.1009906107. Epub 2010 Nov 1.
3
Supervised classification of human microbiota.人类微生物群落的监督分类。
FEMS Microbiol Rev. 2011 Mar;35(2):343-59. doi: 10.1111/j.1574-6976.2010.00251.x. Epub 2010 Oct 7.
4
Individuality in gut microbiota composition is a complex polygenic trait shaped by multiple environmental and host genetic factors.肠道微生物组成的个体差异是一种复杂的多基因特征,由多种环境和宿主遗传因素塑造。
Proc Natl Acad Sci U S A. 2010 Nov 2;107(44):18933-8. doi: 10.1073/pnas.1007028107. Epub 2010 Oct 11.
5
The effect of diet on the human gut microbiome: a metagenomic analysis in humanized gnotobiotic mice.饮食对人类肠道微生物组的影响:在人源化无菌小鼠中的宏基因组分析。
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6
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Alterations of phospholipid concentration and species composition of the intestinal mucus barrier in ulcerative colitis: a clue to pathogenesis.溃疡性结肠炎中肠道黏液屏障的磷脂浓度和种类组成的改变:发病机制的线索。
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Molecular-phylogenetic characterization of microbial community imbalances in human inflammatory bowel diseases.人类炎症性肠病中微生物群落失衡的分子系统发育特征
Proc Natl Acad Sci U S A. 2007 Aug 21;104(34):13780-5. doi: 10.1073/pnas.0706625104. Epub 2007 Aug 15.
10
Microbial source tracking: state of the science.微生物溯源:科学现状
Environ Sci Technol. 2002 Dec 15;36(24):5279-88. doi: 10.1021/es026000b.

人类相关微生物特征:检验其预测价值。

Human-associated microbial signatures: examining their predictive value.

机构信息

Department of Computer Science, University of Colorado, Boulder, CO 80309, USA.

出版信息

Cell Host Microbe. 2011 Oct 20;10(4):292-6. doi: 10.1016/j.chom.2011.09.003.

DOI:10.1016/j.chom.2011.09.003
PMID:22018228
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3879110/
Abstract

Host-associated microbial communities are unique to individuals, affect host health, and correlate with disease states. Although advanced technologies capture detailed snapshots of microbial communities, high within- and between-subject variation hampers discovery of microbial signatures in diagnostic or forensic settings. We suggest turning to machine learning and discuss key directions toward harnessing human-associated microbial signatures.

摘要

宿主相关的微生物群落是个体特有的,会影响宿主的健康,并与疾病状态相关。虽然先进的技术可以捕捉到微生物群落的详细快照,但个体内和个体间的高度变异性阻碍了在诊断或法医学环境中发现微生物特征。我们建议转向机器学习,并讨论利用人类相关微生物特征的关键方向。