Characterization of microbial associations in human oral microbiome.

  title={Characterization of microbial associations in human oral microbiome.},
  author={Min Su Lee and Sangyoon Oh and Haixu Tang},
  journal={Bio-medical materials and engineering},
  volume={24 6},
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… 
3 Citations

Figures from this paper

Detecting interaction networks in the human microbiome with conditional Granger causality
It is shown that correlation is not a good proxy for biological interaction; a weak negative relationship between correlation and causality is observed; and Granger causality and related techniques may be particularly helpful for understanding the driving factors governing microbiome composition and structure.


Microbial Co-occurrence Relationships in the Human Microbiome
An ensemble method based on multiple similarity measures in combination with generalized boosted linear models (GBLMs) to taxonomic marker (16S rRNA gene) profiles of this cohort resulted in a global network of 3,005 significant co-occurrence and co-exclusion relationships between 197 clades occurring throughout the human microbiome.
A framework for human microbiome research
Resources from a population of 242 healthy adults sampled at 15 or 18 body sites up to three times are presented, which have generated 5,177 microbial taxonomic profiles from 16S ribosomal RNA genes and over 3.5 terabases of metagenomic sequence so far.
Microbial interactions: from networks to models
This Review describes how metagenomics and 16S pyrosequencing techniques are opening the way towards global ecosystem network prediction and the development of ecosystem-wide dynamic models.
Network deconvolution as a general method to distinguish direct dependencies in networks
This work presents a general method for inferring direct effects from an observed correlation matrix containing both direct and indirect effects, and introduces an algorithm that removes the combined effect of all indirect paths of arbitrary length in a closed-form solution by exploiting eigen-decomposition and infinite-series sums.
Co-occurrence of anaerobic bacteria in colorectal carcinomas
A polymicrobial signature of Gram-negative anaerobic bacteria is associated with colorectal carcinoma tissue, associated with over-expression of numerous host genes, including the gene encoding the pro-inflammatory chemokine Interleukin-8.
An automated method for finding molecular complexes in large protein interaction networks
A novel graph theoretic clustering algorithm, "Molecular Complex Detection" (MCODE), that detects densely connected regions in large protein-protein interaction networks that may represent molecular complexes is described.
The human microbiome consortium: A framework for human microbiome
  • research, Nature
  • 2012