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A variational bayes beta mixture model for feature selection in DNA methylation studies
KTH, School of Electrical Engineering (EES), Communication Theory.
Statistical Genomics Group, Paul O'Gorman Building, UCL Cancer Institute, University College London.
2013 (English)In: Journal of Bioinformatics and Computational Biology, ISSN 0219-7200, Vol. 11, no 4, 1350005- p.Article in journal (Refereed) Published
Abstract [en]

An increasing number of studies are using beadarrays to measure DNA methylation on a genome-wide basis. The purpose is to identify novel biomarkers in a wide range of complex genetic diseases including cancer. A common difficulty encountered in these studies is distinguishing true biomarkers from false positives. While statistical methods aimed at improving the feature selection step have been developed for gene expression, relatively few methods have been adapted to DNA methylation data, which is naturally beta-distributed. Here we explore and propose an innovative application of a recently developed variational Bayesian beta-mixture model (VBBMM) to the feature selection problem in the context of DNA methylation data generated from a highly popular beadarray technology. We demonstrate that VBBMM offers significant improvements in inference and feature selection in this type of data compared to an Expectation-Maximization (EM) algorithm, at a significantly reduced computational cost. We further demonstrate the added value of VBBMM as a feature selection and prioritization step in the context of identifying prognostic markers in breast cancer. A variational Bayesian approach to feature selection of DNA methylation profiles should thus be of value to any study undergoing large-scale DNA methylation profiling in search of novel biomarkers.

Place, publisher, year, edition, pages
2013. Vol. 11, no 4, 1350005- p.
Keyword [en]
Feature selection, beta mixture, DNA methylation, variational Bayes
National Category
Bioinformatics and Systems Biology Signal Processing
Research subject
SRA - Molecular Bioscience; SRA - ICT
URN: urn:nbn:se:kth:diva-121044DOI: 10.1142/S0219720013500054ISI: 000328163800003ScopusID: 2-s2.0-84879395510OAI: diva2:616502

QC 20130603

Available from: 2013-04-17 Created: 2013-04-17 Last updated: 2014-01-13Bibliographically approved

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Ma, Zhanyu
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Communication Theory
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