MegaR: an interactive R package for rapid sample classification and phenotype prediction using metagenome profiles and machine learning

Background: Diverse microbiome communities drive biogeochemical processes and evolution of animals in their ecosystems. Many microbiome projects have demonstrated the power of using metagenomics to understand the structures and factors influencing the function of the microbiomes in their environment...

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Bibliographic Details
Main Authors:	Ahn, T.-H (Author), Dhungel, E. (Author), Gwak, H.-J (Author), Mreyoud, Y. (Author), Rajeh, A. (Author), Rho, M. (Author)
Format:	Article
Language:	English
Published:	BioMed Central Ltd 2021
Subjects:	16s rrna sequencing Bacteria Biogeochemical process Diagnosis Diagnostic methods Digital storage Ecosystems Forecasting genetics human Humans Interactive visual analysis Interactive visualizations machine learning Machine learning Machine Learning Machine learning models metagenome Metagenome metagenomics Metagenomics phenotype Phenotype Phenotype prediction Prediction accuracy Predictive analytics RNA RNA 16S RNA, Ribosomal, 16S R-package Sample classification User experience Visualization
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MegaR: an interactive R package for rapid sample classification and phenotype prediction using metagenome profiles and machine learning

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