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dc.contributor.authorKällberg, Morten
dc.contributor.authorLu, Hui
dc.date.accessioned2011-05-27T17:47:48Z
dc.date.available2011-05-27T17:47:48Z
dc.date.issued2010-12-07
dc.identifier.bibliographicCitationKallberg, M. & Lu, H. 2010. An improved machine learning protocol for the identification of correct Sequest search results. BMC Bioinformatics, 11: 591. DOI: 10.1186/1471-2105-11-591en
dc.identifier.issn1471-2105
dc.identifier.otherDOI: 10.1186/1471-2105-11-591
dc.identifier.urihttp://hdl.handle.net/10027/7763
dc.description© 2010 Källberg and Lu; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The original works is available through BioMed Central at DOI: 10.1186/1471-2105-11-591en
dc.description.abstractBackground: Mass spectrometry has become a standard method by which the proteomic profile of cell or tissue samples is characterized. To fully take advantage of tandem mass spectrometry (MS/MS) techniques in large scale protein characterization studies robust and consistent data analysis procedures are crucial. In this work we present a machine learning based protocol for the identification of correct peptide-spectrum matches from Sequest database search results, improving on previously published protocols. Results: The developed model improves on published machine learning classification procedures by 6% as measured by the area under the ROC curve. Further, we show how the developed model can be presented as an interpretable tree of additive rules, thereby effectively removing the ‘black-box’ notion often associated with machine learning classifiers, allowing for comparison with expert rule-of-thumb. Finally, a method for extending the developed peptide identification protocol to give probabilistic estimates of the presence of a given protein is proposed and tested. Conclusions: We demonstrate the construction of a high accuracy classification model for Sequest search results from MS/MS spectra obtained by using the MALDI ionization. The developed model performs well in identifying correct peptide-spectrum matches and is easily extendable to the protein identification problem. The relative ease with which additional experimental parameters can be incorporated into the classification framework, to give additional discriminatory power, allows for future tailoring of the model to take advantage of information from specific instrument set-ups.en
dc.description.sponsorshipMK acknowledges support from FMC Technologies Fund Fellowship. We acknowledge the Financial support from University of Illinois at Chicago and China National Basic Research Program 2007CB947800.en
dc.language.isoen_USen
dc.publisherBioMed Centralen
dc.subjectMass spectrometryen
dc.subjecttandem mass spectrometryen
dc.titleAn improved machine learning protocol for the identification of correct Sequest search resultsen
dc.typeArticleen


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