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Thesis

Probabilistic models of RNA secondary structure

Abstract:

This thesis develops probabilistic models of RNA secondary structure. The first chapter introduces RNA secondary structure prediction, in particular stochastic context-free grammars (SCFGs), and considers a novel method for automated design of SCFGs. Many SCFGs are found with a similar predictive quality as those commonly used for RNA secondary structure prediction. The second chapter discusses the effect alignment quality, evolutionary distance between sequences, and number of sequences i...

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Institution:
University of Oxford
Oxford college:
St Catherine's College
Department:
Mathematical,Physical & Life Sciences Division - Statistics
Role:
Author

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Role:
Supervisor
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Funding agency for:
James William Justin Anderson
Publication date:
2013
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
Oxford University, UK
URN:
uuid:3e58e9d9-c58d-4616-8e88-4082d1ca0e2a
Local pid:
ora:7192

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