1：Experimental Design and Method Selection:

A deep neural network composed of several stacked binary restricted Boltzmann machines was designed to learn low-dimensional deep features of DNA methylation data.

2：Sample Selection and Data Sources:

DNA methylation data were obtained from the Gene Expression Omnibus (GEO) website, specifically dataset GSE32393, which includes 136 women breast tissues samples.

3：List of Experimental Equipment and Materials:

Not explicitly mentioned.

4：Experimental Procedures and Operational Workflow:

The selected 5,000 dimensional features were sent to the DNN for dimension reduction. The effect of dimension reduction was examined, and unsupervised clustering analysis was conducted using features extracted from the DNN model.

5：Data Analysis Methods:

The features were clustered by the self-organizing feature maps (SOM) methods, and the effect was compared with traditional methods like PCA and NMF.