A novel K-mer mixture logistic regression for methylation susceptibility modeling of CpG dinucleotides in human gene promoters
Pages 366 - 370
Abstract
DNA methylation is essential for normal cell development and differentiation and plays a crucial role in the development of nearly all types of cancer. Although it is now possible, using next generation sequencing technologies, to assess human methylomes at base resolution, no reports currently exist on modeling cell type-specific DNA methylation susceptibility. Thus, we conducted a comprehensive modeling study of cell type-specific DNA methylation susceptibility at three different resolutions: CpG dinucleotides, CpG segments, and individual gene promoter regions.
Using a k-mer mixture logistic regression model, we effectively modeled DNA methylation susceptibility across five different cell types. The significance of these results is three fold: 1) this is the first report to indicate that CpG methylation susceptible "segments" exist; 2) our model demonstrates the significance of certain k-mers for the mixture model, potentially highlighting DNA sequence features (k-mers) of differentially methylated, promoter CpG island sequences across different tissue types; 3) as only 3 or 4 bp patterns had previously been used for modeling DNA methylation susceptibility, ours is the first demonstration that 6-mer modeling can be performed without loss of accuracy.
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- A novel K-mer mixture logistic regression for methylation susceptibility modeling of CpG dinucleotides in human gene promoters
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Published In
August 2011
688 pages
ISBN:9781450307963
DOI:10.1145/2147805
- General Chairs:
- Robert Grossman,
- Andrey Rzhetsky,
- Program Chairs:
- Sun Kim,
- Wei Wang
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Published: 01 August 2011
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BCB' 11: ACM International Conference on Bioinformatics, Computational Biology and Biomedicine
August 1 - 3, 2011
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