Towards the prediction of protein abundance from tandem mass spectrometry data

Anthony J. Bonner, Han Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings of the Sixth SIAM International Conference on Data Mining
PublisherSociety for Industrial and Applied Mathematics
Pages599-603
Number of pages5
ISBN (Print)089871611X, 9780898716115
DOIs
StatePublished - 2006
EventSixth SIAM International Conference on Data Mining - Bethesda, MD, United States
Duration: Apr 20 2006Apr 22 2006

Publication series

NameProceedings of the Sixth SIAM International Conference on Data Mining
Volume2006

Other

OtherSixth SIAM International Conference on Data Mining
Country/TerritoryUnited States
CityBethesda, MD
Period4/20/064/22/06

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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