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      Deep Neural Network for Analysis of DNA Methylation Data

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          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.

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          A Bayesian Analysis of Some Nonparametric Problems

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              Robust text-independent speaker identification using Gaussian mixture speaker models

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                Author and article information

                Journal
                02 August 2018
                Article
                1808.01359
                53577825-92c8-4207-89b7-5320bf270d12

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                Techinical Report
                q-bio.GN q-bio.QM stat.ML

                Quantitative & Systems biology,Machine learning,Genetics
                Quantitative & Systems biology, Machine learning, Genetics

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