Agglomerative joint clustering of metabolic data with spike at zero: A Bayesian perspective

DOI10.1002/bimj.201400110zbMath1381.62277OpenAlexW1560444140WikidataQ30975691 ScholiaQ30975691MaRDI QIDQ2802567

Mostafa Ghannad-Rezaie, Vahid Partovi Nia

Publication date: 26 April 2016

Published in: Biometrical Journal (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/bimj.201400110

zbMATH Keywords

Bayesian clusteringdendrogramagglomerative clusteringspike-and-slab modelmetabolic data

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Uses Software

• Matlab
• R
• rgl
• bclust
• FABIA

Cites Work

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• Estimating the dimension of a model
• Statistical modeling: The two cultures. (With comments and a rejoinder).
• Biclustering in data mining
• Machine learning versus statistical modeling
• A Laplace mixture model for identification of differential expression in microarray experiments
• Clustering Using Objective Functions and Stochastic Search
• Finding Groups in Data
• A simple model‐based approach to variable selection in classification and clustering
• A Quantitative Study of Gene Regulation Involved in the Immune Response of Anopheline Mosquitoes

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