Fecal source identification using random forest.

Adélaïde RoguetA Murat ErenRyan J NewtonSandra L McLellan

Published in: Microbiome (2018)

Random forest classification with 16S rRNA gene amplicons offers a rapid, sensitive, and accurate solution for identifying host microbial signatures to detect human and animal fecal contamination in environmental samples.

Keyphrases

climate change
human health
genome wide
endothelial cells
machine learning
deep learning
risk assessment
microbial community
copy number
drinking water
high resolution
dna methylation
neural network
health risk
loop mediated isothermal amplification
bioinformatics analysis
sensitive detection