A joint finite mixture model for clustering genes from independent Gaussian and beta distributed data |
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Authors: | Xiaofeng Dai Timo Erkkil? Olli Yli-Harja and Harri L?hdesm?ki |
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Institution: | (1) Department of Signal Processing, Tampere University of Technology, Tampere, Finland |
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Abstract: | Background Cluster analysis has become a standard computational method for gene function discovery as well as for more general explanatory
data analysis. A number of different approaches have been proposed for that purpose, out of which different mixture models
provide a principled probabilistic framework. Cluster analysis is increasingly often supplemented with multiple data sources
nowadays, and these heterogeneous information sources should be made as efficient use of as possible. |
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