 Epigenetic processes such as DNA methylation  are increasingly recognised for their fundamental role in diseases such as cancer. Changes in DNA methylation patterns reflect environmental risk factors, and are amongst the first pre-disease changes in cancer. Hence, DNA methylation appears highly promising as a basis on which to develop minimally invasive, DNA-based measures of disease risk and prognosis. 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, as continues to happen in the genome-wide era of biomedical science. We present a DNA methylation-based measure of genomic interaction and association , and we show how to use it to infer prognostic genomic networks. We show how to identify prognostic biomarkers from such networks, which we term `network community oncomarkers'. These findings represent new statistical tools for use in the biomedical sciences .
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 data-set .
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In this paper we propose network methodology to infer prognostic cancer biomarkers, based on the epigenetic pattern DNA methylation.
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reflect environmental risk factors, and
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Changes in DNA methylation patterns reflect environmental risk factors, and are amongst the first pre-disease changes in cancer. Hence, DNA methylation appears highly promising as a basis on which to develop minimally invasive, DNA-based measures of disease risk and prognosis. DNA methylation
DNA methylation
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as continues to happen in the genome-wide era of biomedical science. We present
making them increasingly relevant to large-scale genomic studies. We propose methodology to infer prognostic genomic networks from
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, and we show how to use it to infer prognostic genomic networks. We
. We then
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These findings represent new statistical tools for use in the biomedical sciences
We illustrate the power of our proposed methodology in the context of a large publicly available breast cancer data-set