Characterization of microbial associations in human oral microbiome. Su, Min, L., Sangyoon, O., & Haixu, T. Bio-Medical Materials and Engineering, 2014. ![Characterization of microbial associations in human oral microbiome [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Microorganisms interact with each other within a community. Within the same community, some microorganisms tend to co-exist, whereas some others tend to avoid each other. The association among microorganisms can be revealed by computing the correlation between their abundance patterns that are measured through metagenomic sequencing across multiple communities. In this paper, we built an association network among microorganisms from the human oral microbiome. To improve its accuracy, we adopted a network deconvolution algorithm to filter out indirect associations, and we used an ensemble of three correlation measures to filter out the false-positive associations. When applying on the metagenomic data from human oral samples, experimental results showed that phylogenetically close microorganisms formed highly correlated network clusters. Additionally, most of the identified mutually exclusive associations were related to the order Lactobacillales.
@article{ su_characterization_2014,
title = {Characterization of microbial associations in human oral microbiome},
copyright = {©2014 \© IOS Press and the authors.},
issn = {0959-2989},
url = {http://www.medra.org/servlet/aliasResolver?alias=iospress&genre=article&issn=0959-2989&volume=24&issue=6&spage=3737&doi=10.3233/BME-141202},
doi = {10.3233/BME-141202},
abstract = {Microorganisms interact with each other within a community. Within the same community, some microorganisms tend to co-exist, whereas some others tend to avoid each other. The association among microorganisms can be revealed by computing the correlation between their abundance patterns that are measured through metagenomic sequencing across multiple communities. In this paper, we built an association network among microorganisms from the human oral microbiome. To improve its accuracy, we adopted a network deconvolution algorithm to filter out indirect associations, and we used an ensemble of three correlation measures to filter out the false-positive associations. When applying on the metagenomic data from human oral samples, experimental results showed that phylogenetically close microorganisms formed highly correlated network clusters. Additionally, most of the identified mutually exclusive associations were related to the order Lactobacillales.},
number = {6},
urldate = {2015-06-26TZ},
journal = {Bio-Medical Materials and Engineering},
author = {Su, Lee Min and Sangyoon, Oh and Haixu, Tang},
year = {2014},
keywords = {collectionenterotype microbiome},
pages = {3737--3744}
}
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