Abstract
Researches on the microbiome have been actively conducted
worldwide and the results have shown human gut bacterial
environment significantly impacts on immune system, psychological
conditions, cancers, obesity, and metabolic diseases.
Thanks to the development of sequencing technology,
microbiome studies with large number of samples are eligible
on an acceptable cost nowadays. Large samples allow analysis
of more sophisticated modeling using machine learning
approaches to study relationships between microbiome and
various traits. This article provides an overview of machine
learning methods for non-data scientists interested in the association
analysis of microbiomes and host phenotypes. Once
genomic feature of microbiome is determined, various analysis
methods
can be used to explore the relationship between
microbiome and host phenotypes that include penalized regression,
support vector machine (SVM), random forest, and
artificial neural network (ANN). Deep neural network methods
are also touched. Analysis procedure from environment
setup to extract analysis results are presented with Python
programming language.
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