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Rasmussen et al., 2008 - Google Patents

Modeling and visualizing uncertainty in gene expression clusters using Dirichlet process mixtures

Rasmussen et al., 2008

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Document ID
5074655160437935494
Author
Rasmussen C
De la Cruz B
Ghahramani Z
Wild D
Publication year
Publication venue
IEEE/ACM transactions on computational biology and bioinformatics

External Links

Snippet

Although the use of clustering methods has rapidly become one of the standard computational approaches in the literature of microarray gene expression data, little attention has been paid to uncertainty in the results obtained. Dirichlet process mixture …
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Classifications

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    • G06F19/12—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for modelling or simulation in systems biology, e.g. probabilistic or dynamic models, gene-regulatory networks, protein interaction networks or metabolic networks
    • G—PHYSICS
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    • G06F19/20—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for hybridisation or gene expression, e.g. microarrays, sequencing by hybridisation, normalisation, profiling, noise correction models, expression ratio estimation, probe design or probe optimisation
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