Supervised classification of human microbiota.
By Dan Knights, E. Costello, Rob Knight. 2011
443 citations recorded by the source corpus.
Subfields: Molecular Biology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Molecular Biology
- Rob Knight
- David S. Wishart
- Jianguo Xia
- J. Gregory Caporaso
- William Walters
- Ruth E. Ley
- Nicholas A. Bokulich
- Omry Koren
Citations in the corpus
- A comprehensive evaluation of multicategory classification methods for microbiomic data (2013, methodology): “took an excellent first step in this direction and established the feasibility of creating accurate models for classification of body sites and subject identification [2].”
- METAGENassist: a comprehensive web server for comparative metagenomics (2012, methodology): “Supervised classification Supervised methods are becoming increasingly important for discrimination analysis in comparative metagenomics (17).”
- Dirichlet Multinomial Mixtures: Generative Models for Microbial Metagenomics (2012, methodology): “Two studies applying classification methods to microbial communities have appeared recently [19,20].”
- Improved annotation of antibiotic resistance determinants reveals microbial resistomes cluster by ecology (2014, methodology): “In order to determine the Resfams families that most discriminate antibiotic resistomes between ecologies, we used the supervised learning technique Random Forests (Knights et al., 2011).”
- The Impact of the Gut Microbiota on Human Health: An Integrative View (2012, unknown): “The application of these methods will hopefully more accurately predict disease states and appropriate therapies by making use of larger numbers of samples across populations and over time (Knights et al., 2011).”
- The Soil Microbiome Influences Grapevine-Associated Microbiota (2015, methodology): “A supervised learning analysis (52) was conducted using the Random”
- Defining the Human Microbiome (2012, methodology): “Another powerful tool is the use of machine learning and statistical techniques to build predictive models of taxa[25] or functions[26] that discriminate between groups of samples.”