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A mixture model for the evolution of gene expression in non-homogeneous datasets

Abstract:

We address the challenge of assessing conservation of gene expression in complex, non-homogeneous datasets. Recent studies have demonstrated the success of probabilistic models in studying the evolution of gene expression in simple eukaryotic organisms such as yeast, for which measurements are typically scalar and independent. Models capable of studying expression evolution in much more complex organisms such as vertebrates are particularly important given the medical and scientific interest ...

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Institution:
University of Oxford
Department:
Oxford, MPLS, Statistics
Role:
Author
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Journal:
Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference
Pages:
1297-1304
Publication date:
2009-01-01
URN:
uuid:0fe516f4-16d9-4081-9b2e-ce76bd6347e0
Source identifiers:
353237
Local pid:
pubs:353237
Language:
English

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