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Thesis

Computationally intensive methods for Hidden Markov Models with applications to statistical genetics

Abstract:

In most fields of technology and science, the exponential increase of available data is an apparent trend. In genetics, the main contributor to this trend is the improving efficiency of sequencing technologies. While the Human Genome project focused on assembling a single reference sequence not long ago, now there are aims to sequence million genomes in upcoming projects.

The consequent computational challenge is being able to utilise this wealth of data, which requires the dev...

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Peter D. Kecskemethy More by this author

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Role:
Supervisor
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford

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