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Activity Number: 225 - The Human Microbiome: From Discovery Studies to Statistical Predictive Personalized Medicine
Type: Topic Contributed
Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #301721
Title: The Machine Learning Methods Review for Microbiome Host Trait Prediction
Author(s): Yi-Hui Zhou*
Companies: North Carolina State University
Keywords: disease; phenotype; modeling; prediction; machine learning

With the growing importance of microbiome research, there is increasing evidence that host variation in microbial communities is associated with overall host health. Advancement in genetic sequencing methods for microbiomes has coincided with improvements in machine learning, with important implications for disease risk prediction in humans. One aspect specific to microbiome prediction is the use of taxonomy-informed feature selection. In this review for non-experts, we explore the most commonly used machine learning methods, and evaluate their prediction accuracy as applied to microbiome host trait prediction. Methods are described at an introductory level, and R/Python code for the analyses is provided.

Authors who are presenting talks have a * after their name.

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