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Data mining techniques for SELDI MS proteomic data -- Dissertation
Asha Serah, Thomas
ProQuest / UMI
Monday, August 21, 2006
Number of Pages:
The goal of this study was to review and explore artificial intelligence strategies on proteomic data from mass spectrometry. Serum samples collected from congestive heart failure patients and controls are analyzed in this study using linear and non linear decision models. The models had similar performance and low correlation and therefore the models were combined to develop a fusion model that resulted in superior performance than the individual models. The thesis is organized as follows. First, a brief introduction to data mining and proteomics is provided. Then, the need for applying data mining to proteomic data from mass spectrometry is explained. Thereafter, important steps in the mining process are outlined and explained with examples drawn from recent studies involving Surface Enhanced Laser Desorption Ionization Mass Spectrometry data. Terminologies and popular strategies in mining of mass spectral proteomic data are explained. Finally, the data analysis conducted on the dataset is described and results are discussed.
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