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- W2017810217 abstract "As a key regulatory mechanism of gene expression, DNA methylation patterns are widely altered in many complex genetic diseases, including cancer. DNA methylation is naturally quantified by bounded support data; therefore, it is non-Gaussian distributed. In order to capture such properties, we introduce some non-Gaussian statistical models to perform dimension reduction on DNA methylation data. Afterwards, non-Gaussian statistical model-based unsupervised clustering strategies are applied to cluster the data. Comparisons and analysis of different dimension reduction strategies and unsupervised clustering methods are presented. Experimental results show that the non-Gaussian statistical model-based methods are superior to the conventional Gaussian distribution-based method. They are meaningful tools for DNA methylation analysis. Moreover, among several non-Gaussian methods, the one that captures the bounded nature of DNA methylation data reveals the best clustering performance." @default.
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- W2017810217 date "2014-06-16" @default.
- W2017810217 modified "2023-10-16" @default.
- W2017810217 title "Comparisons of Non-Gaussian Statistical Models in DNA Methylation Analysis" @default.
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- W2017810217 doi "https://doi.org/10.3390/ijms150610835" @default.
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