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Related Experiment Videos

A string pattern regression algorithm and its application to pattern discovery in long introns.

Hideo Bannai1, Shunsuke Inenaga, Ayumi Shinohara

  • 1Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokane-dai, Minato-ku, Tokyo 108-8639, Japan. bannai@ims.u-tokyo.ac.jp

Genome Informatics. International Conference on Genome Informatics
|October 23, 2003
PubMed
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We developed string pattern regression to find distinct data subsets and splitting rules. This method identifies unique biological patterns in DNA sequences and has potential applications in gene expression analysis.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Mining

Background:

  • Discovering patterns in complex datasets is crucial for scientific insight.
  • Identifying subsets with distinct numerical attributes requires effective pattern discovery methods.

Purpose of the Study:

  • To introduce string pattern regression for identifying data subsets with distinct numerical attribute distributions.
  • To develop an efficient algorithm for discovering conserved string patterns within these subsets.

Main Methods:

  • String pattern regression: a novel approach correlating string patterns with numerical attribute distributions.
  • Branch-and-bound algorithm: an exact and efficient method for general pattern classes.
  • Application to biological data: analysis of intron sequences from human, mouse, fly, and zebrafish.

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Main Results:

  • Demonstrated the practicality of string pattern regression and the branch-and-bound algorithm.
  • Successfully identified distinct subsets and conserved string patterns in biological sequences.
  • Showcased the method's ability to find subsets with significantly different numerical attributes.

Conclusions:

  • String pattern regression is a powerful tool for simultaneous subset and rule discovery.
  • The developed algorithm is efficient and applicable to various pattern classes.
  • Potential applications include analyzing DNA sequences and microarray gene expression data.