Definition of valid proteomic biomarkers: a bayesian solution

Harris, K., Girolami, M. and Mischak, H. (2009) Definition of valid proteomic biomarkers: a bayesian solution. Lecture Notes in Computer Science, 5780, pp. 137-149. (doi: 10.1007/978-3-642-04031-3_13)

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Clinical proteomics is suffering from high hopes generated by reports on apparent biomarkers, most of which could not be later substantiated via validation. This has brought into focus the need for improved methods of finding a panel of clearly defined biomarkers. To examine this problem, urinary proteome data was collected from healthy adult males and females, and analysed to find biomarkers that differentiated between genders. We believe that models that incorporate sparsity in terms of variables are desirable for biomarker selection, as proteomics data typically contains a huge number of variables (peptides) and few samples making the selection process potentially unstable. This suggests the application of a two-level hierarchical Bayesian probit regression model for variable selection which assumes a prior that favours sparseness. The classification performance of this method is shown to improve that of the Probabilistic K-Nearest Neighbour model.

Item Type:Articles
Keywords:Proteomic biomarkers; classification; sparsity; feature selection; Bayesian inference
Glasgow Author(s) Enlighten ID:Mischak, Professor Harald and Girolami, Prof Mark and Harris, Dr Keith
Authors: Harris, K., Girolami, M., and Mischak, H.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QH Natural history > QH345 Biochemistry
H Social Sciences > HA Statistics
College/School:College of Medical Veterinary and Life Sciences > School of Cardiovascular & Metabolic Health
College of Science and Engineering > School of Computing Science
Journal Name:Lecture Notes in Computer Science
Publisher:Springer Berlin / Heidelberg
ISSN (Online):1611-3349
Published Online:31 August 2009
Copyright Holders:Copyright © 2009 Springer
First Published:First published in Lecture Notes in Computer Science 5780:137-149
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher.

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