High-accuracy Peptide mass fingerprinting using peak intensity data with machine learning.

High-accuracy Peptide mass fingerprinting using peak intensity data with machine learning. Research Abstract Details 

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High-accuracy Peptide mass fingerprinting using peak intensity data with machine learning. Abstract Text:

Dongmei Yang,Kevin Ramkissoon,Eric Hamlett,Morgan C Giddings,Dongmei Yang,Kevin Ramkissoon,Eric Hamlett,Morgan C Giddings,

For MALDI-TOF mass spectrometry, we show that the intensity of a peptide-ion peak is directly correlated with its sequence, with the residues M, H, P, R, and L having the most substantial effect on ionization. We developed a machine learning approach that exploits this relationship to significantly improve peptide mass fingerprint (PMF) accuracy based on training data sets from both true-positive and false-positive PMF searches. The model's cross-validated accuracy in distinguishing real versus false-positive database search results is 91%, rivaling the accuracy of MS/MS-based protein identification.

High-accuracy Peptide mass fingerprinting using peak intensity data with machine learning. Publishing Authors By Initials

D Yang,K Ramkissoon,E Hamlett,MC Giddings,D Yang,K Ramkissoon,E Hamlett,MC Giddings,

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High-accuracy Peptide mass fingerprinting using peak intensity data with machine learning. Journal Published:

PUBLICATION TYPE: Journal Article

Journal: Journal of proteome research

VOLUME: 7

Page Numbers: 62-9

Journal Abbreviation: J. Proteome Res.

ISSN: 1535-3893

DAY: 3

MONTH: 10

YEAR: 2007

High-accuracy Peptide mass fingerprinting using peak intensity data with machine learning. Information

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LANGUAGE: eng

NlmUniqueID: 101128775

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AFFILIATION: giddings@unc.edu.

Country: United States

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MEDLINETA: J Proteome Res

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