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A Review and Tutorial of Machine Learning Methods for Microbiome Host Trait Prediction.

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Title: A Review and Tutorial of Machine Learning Methods for Microbiome Host Trait Prediction.
Authors: Zhou, Yi-Hui, Gallins, Paul
Source: Frontiers in Genetics; 6/25/2019, pN.PAG-N.PAG, 14p
Subject Terms: MACHINE learning, FEATURE selection, PYTHON programming language, MICROBIAL communities
Abstract: 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. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
Description
Abstract: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. [ABSTRACT FROM AUTHOR]
ISSN:16648021
DOI:10.3389/fgene.2019.00579