Discovering patterns of DNA methylation: rule mining with rough sets and decision trees, and comethylation analysis

  • Authors:

  • Niu Ben;Qiang Yang;Jinyan Li;Shiu Chi-keung;Sankar Pal

  • Affiliations:

  • Department of Computer Science and Engineering, Hong Kong University of Science & Technology, Hong Kong, China;Department of Computer Science and Engineering, Hong Kong University of Science & Technology, Hong Kong, China;Institute for Infocomm Research, Singapore;Department of Computing, Hong Kong Polytechnic University, Hong Kong, China;Indian Statistical Institute, Kolkata, India

  • Venue:
  • PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
  • Year:
  • 2007

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Abstract

DNA methylation regulates the transcription of genes without changing their coding sequences. It plays a vital role in the process of embryogenesis and tumorgenesis. To gain more insights into how such epigenetic mechanism works in the human cells, we apply the two popular data mining techniques, i.e., Rough Sets, and Decision Trees, to uncover the logical rules of DNA methylation. Our results show that the Rough Sets method can generate and utilize fewer rules to fully separate the methylation dataset, whereas Decision Trees method relies on more rules but involves fewer decision variables to do the same task. We also find that some of the gene promoters are highly comethylated, demonstrating the evidence that genes are highly interactive epigenetically in human cells.