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Related Concept Videos

Introduction to the Human Microbiota01:22

Introduction to the Human Microbiota

Microorganisms colonize various regions of the human body, including the mouth, nasal passages, throat, stomach, intestines, urogenital tract, and skin. The total number of microbial cells is estimated to range from 10¹³ to 10¹⁴—comparable to, or exceeding, the number of human somatic cells. This host–microbiome relationship has led to the conceptualization of humans as supraorganisms, wherein microbial communities perform vital roles in development, immunity, and disease...
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The human microbiota begins developing at birth and undergoes continual change as we age. Infancy marks a critical period of microbial sensitivity, offering a “window of opportunity” during which beneficial microbes help mature the immune system. By age three, children typically develop a more stable and diverse microbial community. Newborns acquire microbes from their immediate environment; vaginal delivery favors maternal vaginal microbes, while cesarean births favor microbes from the skin...
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The large intestine hosts the most densely populated microbial ecosystem in the human body. This complex community primarily consists of anaerobic bacteria, with Bacillota (formerly Firmicutes) and Bacteroidota (formerly Bacteroidetes) as the predominant groups. The distribution of these microbes varies along different sections of the large intestine, influenced by local environmental factors such as oxygen availability and nutrient composition.The cecum, located at the beginning of the large...
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Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
11:22

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Published on: October 15, 2019

Supervised classification of human microbiota.

Dan Knights1, Elizabeth K Costello, Rob Knight

  • 1Department of Computer Science, University of Colorado, Boulder, CO, USA. rob@spot.colorado.edu

FEMS Microbiology Reviews
|November 3, 2010
PubMed
Summary
This summary is machine-generated.

Machine learning classifiers can effectively analyze human-associated microbial communities. These methods identify key microbial taxa for disease state classification and build accurate predictive models.

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Computational Biology

Background:

  • DNA sequencing advances generate large-scale, high-dimensional data for human-associated microbial communities.
  • Identifying microbial taxa linked to host physiological or disease states is challenging due to rare taxa and high diversity.
  • Traditional analysis methods struggle with the complexity of microbial community data.

Purpose of the Study:

  • To demonstrate the effectiveness of supervised machine learning classifiers for microbiota classification.
  • To identify discriminative microbial taxa for classifying community types.
  • To build accurate models for classifying unlabeled microbial community data.

Main Methods:

  • Application of existing supervised machine learning classifiers (e.g., from microarray analysis, text classification).
  • Utilizing machine learning to select subsets of highly discriminative microbial taxa.
  • Developing models for accurate classification of microbial community data.

Main Results:

  • Supervised classifiers effectively classify microbiota composition.
  • Identification of key microbial taxa associated with different host states.
  • Development of accurate predictive models for unlabeled microbial data.

Conclusions:

  • Machine learning offers powerful tools for analyzing complex microbial community data.
  • Supervised classification can overcome limitations of traditional approaches in microbiota research.
  • This approach aids in understanding host-microbe interactions and disease mechanisms.