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CpG Island Identification with Higher Order and Variable Order Markov Models

In: Data Mining in Biomedicine

Author

Listed:
  • Zhenqiu Liu

    (TATRC)

  • Dechang Chen

    (Uniformed Services University of the Health Sciences)

  • Xue-wen Chen

    (The University of Kansas)

Abstract

Identifying the location and function of human genes in a long sequence of genome is difficult due to lack of sufficient information about genes. Experimental evidence has suggested that there exists strong correlation between CpG islands and genes immediately following them. Much research has been done to identify CpG islands in a DNA sequence using various models. In this chapter, we introduce two alternative models based on high order and variable order Markov chains. Compared with the popular models such as the first order Markov chain, HMM, and HMT, these two models are much easier to compute and have higher identification accuracies. One unsolved problem with the Markov model is that there is no way to decide the exact boundary point between CpG and non-CpG islands. In this chapter, we provide a novel tool to decide the boundary points using the sequential probability test. Sequential data from GeneBank are used for the experiments in this chapter.

Suggested Citation

  • Zhenqiu Liu & Dechang Chen & Xue-wen Chen, 2007. "CpG Island Identification with Higher Order and Variable Order Markov Models," Springer Optimization and Its Applications, in: Panos M. Pardalos & Vladimir L. Boginski & Alkis Vazacopoulos (ed.), Data Mining in Biomedicine, pages 47-57, Springer.
    • Handle: RePEc:spr:spochp:978-0-387-69319-4_4
    DOI: 10.1007/978-0-387-69319-4_4
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