Deep Neural Network for Analysis of DNA Methylation Data
This work addresses the challenge of distinguishing tumor subtypes using DNA methylation data, which is important for cancer research and diagnosis, but it is incremental as it applies an existing deep learning approach to a specific domain.
The authors tackled the problem of analyzing high-dimensional DNA methylation data for cancer subtype classification by designing a deep neural network with stacked binary restricted Boltzmann machines, achieving the best performance in breast cancer DNA methylation data cluster analysis compared to state-of-the-art methods.
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.