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STATISTICAL ANALYSIS OF PROTEOMIC MASS SPECTROMETRY DATA FOR THE IDENTIFICATION OF BIOMARKERS AND DISEASE DIAGNOSIS

Part of: Linear inference, regression Multivariate analysis Applications

Published online by Cambridge University Press:  16 August 2016

TYMAN E. STANFORD
TYMAN E. STANFORD*
Affiliation:
School of Mathematical Sciences, The University of Adelaide, SA 5005, Australia email tyman.stanford@adelaide.edu.au
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Abstract

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Keywords

mass spectraproteomicsbiomarkersimage analysispre-processingmissing valuessignal smoothingbaseline correctionnormalisationpeak detectionpeak alignmentlinear modelssupervised learning

MSC classification

Primary: 62P10: Applications to biology and medical sciences
Secondary: 62H30: Classification and discrimination; cluster analysis 62H35: Image analysis 62J12: Generalized linear models
Type
Abstracts of Australasian PhD Theses
Information
Bulletin of the Australian Mathematical Society , Volume 94 , Issue 2 , October 2016 , pp. 345 - 346
Copyright
© 2016 Australian Mathematical Publishing Association Inc. 

References

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Kulasingam, V. and Diamandis, E. P., ‘Strategies for discovering novel cancer biomarkers through utilization of emerging technologies’, Nat. Clin. Pract. Oncol. 5(10) (2008), 588–599.Google Scholar