TY - GEN
T1 - Towards the prediction of protein abundance from tandem mass spectrometry data
AU - Bonner, Anthony J.
AU - Liu, Han
PY - 2006
Y1 - 2006
N2 - This paper 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 method that uses simpler 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). We have developed and evaluated several techniques for tackling this problem, including the development of three generative models of MS/MS data, and methods for efficiently fitting the models to data. In addition, we tested each method on three real-world datasets generated by MS/MS experiments performed on various tissue samples taken from Mouse. This paper outlines the biological problem and presents a selection of our results.
AB - This paper 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 method that uses simpler 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). We have developed and evaluated several techniques for tackling this problem, including the development of three generative models of MS/MS data, and methods for efficiently fitting the models to data. In addition, we tested each method on three real-world datasets generated by MS/MS experiments performed on various tissue samples taken from Mouse. This paper outlines the biological problem and presents a selection of our results.
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U2 - 10.1137/1.9781611972764.68
DO - 10.1137/1.9781611972764.68
M3 - Conference contribution
AN - SCOPUS:33745437093
SN - 089871611X
SN - 9780898716115
T3 - Proceedings of the Sixth SIAM International Conference on Data Mining
SP - 599
EP - 603
BT - Proceedings of the Sixth SIAM International Conference on Data Mining
PB - Society for Industrial and Applied Mathematics
T2 - Sixth SIAM International Conference on Data Mining
Y2 - 20 April 2006 through 22 April 2006
ER -