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
CountryUnited States
CityBethesda, MD
Period4/20/064/22/06

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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  • Cite this

    Bonner, A. J., & Liu, H. (2006). Towards the prediction of protein abundance from tandem mass spectrometry data. In Proceedings of the Sixth SIAM International Conference on Data Mining (pp. 599-603). (Proceedings of the Sixth SIAM International Conference on Data Mining; Vol. 2006). Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611972764.68