Statistical models and computational algorithms for discovering relationships in microbiome data

DOI10.1515/sagmb-2015-0096zbMath1360.92013OpenAlexW2563004743WikidataQ31151809 ScholiaQ31151809MaRDI QIDQ523934

Joseph Beyene, Mateen R. Shaikh

Publication date: 25 April 2017

Published in: Statistical Applications in Genetics and Molecular Biology (Search for Journal in Brave)

Full work available at URL: https://www.degruyter.com/view/j/sagmb.2017.16.issue-1/sagmb-2015-0096/sagmb-2015-0096.xml?format=INT

zbMATH Keywords

model selectioncompositionconstraintsevolutionary algorithmsmicrobiomeDirichlet-multinomial

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) General biostatistics (92B15) Problems related to evolution (92D15)

Uses Software

• mclust

Cites Work

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• Consistent estimation of a mixing distribution
• Overdispersion in allelic counts and \(\theta\)-correction in forensic genetics
• Variable selection for sparse Dirichlet-multinomial regression with an application to microbiome data analysis
• MCLUST: Software for model-based cluster analysis
• Evolutionary Computation for Modeling and Optimization
• How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
• Model-Based Gaussian and Non-Gaussian Clustering
• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• Concepts of Independence for Proportions with a Generalization of the Dirichlet Distribution

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