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September 2016 Detection of epigenomic network community oncomarkers
Thomas E. Bartlett, Alexey Zaikin
Ann. Appl. Stat. 10(3): 1373-1396 (September 2016). DOI: 10.1214/16-AOAS939

Abstract

In this paper we propose network methodology to infer prognostic cancer biomarkers based on the epigenetic pattern DNA methylation. Epigenetic processes such as DNA methylation reflect environmental risk factors, and are increasingly recognised for their fundamental role in diseases such as cancer. DNA methylation is a gene-regulatory pattern, and hence provides a means by which to assess genomic regulatory interactions. Network models are a natural way to represent and analyse groups of such interactions. The utility of network models also increases as the quantity of data and number of variables increase, making them increasingly relevant to large-scale genomic studies. We propose methodology to infer prognostic genomic networks from a DNA methylation-based measure of genomic interaction and association. We then show how to identify prognostic biomarkers from such networks, which we term “network community oncomarkers”. We illustrate the power of our proposed methodology in the context of a large publicly available breast cancer dataset.

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Thomas E. Bartlett. Alexey Zaikin. "Detection of epigenomic network community oncomarkers." Ann. Appl. Stat. 10 (3) 1373 - 1396, September 2016. https://doi.org/10.1214/16-AOAS939

Information

Received: 1 July 2015; Revised: 1 March 2016; Published: September 2016
First available in Project Euclid: 28 September 2016

zbMATH: 06775270
MathSciNet: MR3553228
Digital Object Identifier: 10.1214/16-AOAS939

Keywords: Community detection , Computational biology , epigenomics , Stochastic networks

Rights: Copyright © 2016 Institute of Mathematical Statistics

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Vol.10 • No. 3 • September 2016
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