Deep Neural Network for Analysis of DNA Methylation Data

TL;DR

This paper introduces a deep neural network model using stacked binary restricted Boltzmann machines to analyze high-dimensional DNA methylation data, improving tumor subtype classification, especially in breast cancer.

Contribution

The novel deep neural network architecture explicitly captures properties of DNA methylation data, outperforming existing methods in tumor subtype clustering.

Findings

Deep features improve clustering accuracy.

Model outperforms state-of-the-art methods.

Effective in breast cancer methylation analysis.

Abstract

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.