Development and evaluation of methods for predicting protein levels from tandem mass spectrometry data.
Published: 2005
Language: eng
Number of pages: 122
Description: This work addresses a central problem of Proteomics: estimating the amounts of each of the thousands of proteins in a cell culture or tissue sample. Although laboratory methods involving isotopes have been developed for this problem, we seek a simpler approach, one that uses more-straightforward laboratory procedures. Specifically, our aim is to use data-mining techniques to infer protein levels from the relatively cheap and abundant data available from high-throughput tandem mass spectrometry (MS/MS). In this thesis, we develop and evaluate several techniques for tackling this problem. Specifically, we develop and evaluate different statistical models of MS/MS data. In addition, to evaluate their biological relevance, we test each method on three real-world datasets generated by MS/MS experiments performed on various tissue samples taken from Mouse.
Notes: Thesis (M.Sc.)--University of Toronto, 2005.

Electronic version licensed for access by U. of T. users.

Source: Masters Abstracts International, Volume: 44-01, page: 0398.

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