  Many researches demonstrated that the DNA methylation, which occurs in the
context of a CpG, has strong correlation with diseases, including cancer. There
is a strong interest in analyzing the DNA methylation data to find how to
distinguish different subtypes of the tumor. However, the conventional
statistical methods are not suitable for analyzing the highly dimensional DNA
methylation data with bounded support. In order to explicitly capture the
properties of the data, we design a deep neural network, which composes of
several stacked binary restricted Boltzmann machines, to learn the low
dimensional deep features of the DNA methylation data. Experiments show these
features perform best in breast cancer DNA methylation data cluster analysis,
comparing with some state-of-the-art methods.
