Model-based clustering with gene ranking using penalized mixtures of heavy-tailed distributions
10.1142/S0219720013410072
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sg-nus-scholar.10635-1250642023-10-25T22:48:26Z Model-based clustering with gene ranking using penalized mixtures of heavy-tailed distributions Cozzini, A. Jasra, A. Montana, G. STATISTICS & APPLIED PROBABILITY Clustering gene selection microarray data mixture models skewed distributions 10.1142/S0219720013410072 Journal of Bioinformatics and Computational Biology 11 3 - JBCBB 2016-06-02T10:30:22Z 2016-06-02T10:30:22Z 2013-06 Conference Paper Cozzini, A., Jasra, A., Montana, G. (2013-06). Model-based clustering with gene ranking using penalized mixtures of heavy-tailed distributions. Journal of Bioinformatics and Computational Biology 11 (3) : -. ScholarBank@NUS Repository. https://doi.org/10.1142/S0219720013410072 02197200 http://scholarbank.nus.edu.sg/handle/10635/125064 000321005400007 Scopus |
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Clustering gene selection microarray data mixture models skewed distributions |
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Clustering gene selection microarray data mixture models skewed distributions Cozzini, A. Jasra, A. Montana, G. Model-based clustering with gene ranking using penalized mixtures of heavy-tailed distributions |
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10.1142/S0219720013410072 |
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STATISTICS & APPLIED PROBABILITY |
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STATISTICS & APPLIED PROBABILITY Cozzini, A. Jasra, A. Montana, G. |
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Conference or Workshop Item |
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Cozzini, A. Jasra, A. Montana, G. |
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Cozzini, A. |
title |
Model-based clustering with gene ranking using penalized mixtures of heavy-tailed distributions |
title_short |
Model-based clustering with gene ranking using penalized mixtures of heavy-tailed distributions |
title_full |
Model-based clustering with gene ranking using penalized mixtures of heavy-tailed distributions |
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Model-based clustering with gene ranking using penalized mixtures of heavy-tailed distributions |
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Model-based clustering with gene ranking using penalized mixtures of heavy-tailed distributions |
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model-based clustering with gene ranking using penalized mixtures of heavy-tailed distributions |
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2016 |
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http://scholarbank.nus.edu.sg/handle/10635/125064 |
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