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A more recent version of this article appeared on March 1, 2006.
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Submitted on July 27, 2005
Revised on November 10, 2005
Accepted on November 30, 2005

Improved classification of mass spectrometry database search results using newer machine learning approaches

Peter J. Ulintz, Ji Zhu, Zhaohui S. Qin, and Philip C. Andrews

Biological Chemistry, University of Michigan, Ann Arbor, MI 48109

Corresponding Author: pulintz{at}umich.edu

Manual analysis of mass spectrometry data is a current bottleneck in high-throughput proteomics. In particular, the need to manually validate the results of mass spectrometry database searching algorithms can be prohibitively time-consuming. Development of software tools that attempt to quantify the confidence in the assignment of a protein or peptide identity to a mass spectrum is an area of active interest. We seek to extend work in this area by investigating the potential of recent machine learning algorithms to improve the accuracy of these approaches, and as a flexible framework for accommodating new data features. Specifically, we demonstrate the ability of boosting and random forest approaches to improve the discrimination of true hits from false positive identifications in the results of mass spectrometry database search engines, compared to thresholding and other machine learning approaches. We accommodate additional attributes obtainable from database search results, including a factor addressing proton mobility. Performance is evaluated using publically available electrospray data and a new collection of MALDI data generated from purified human reference proteins.


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