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Integer programming-based method for grammar-based tree compression and its application to pattern extraction of

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This study introduces integer programming methods to find minimum context-free grammars (CFGs) and tree grammars for data compression and pattern extraction in biological structures like glycans.

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Data compression

Background:

  • Recent advancements in grammar-based compression for tree-structured data introduced elementary ordered-tree grammars (EOTGs) and elementary unordered-tree grammars (EUTGs).
  • Existing approximation algorithms for these grammars have limitations.

Purpose of the Study:

  • To develop precise methods for finding minimum context-free grammars (CFGs), EOTGs, and EUTGs.
  • To apply these methods for pattern extraction in complex biological structures, specifically glycans.

Main Methods:

  • An integer programming-based approach was developed to find minimum CFGs for strings with restricted production rules.
  • This method was extended to determine minimum EOTGs and EUTGs for ordered and unordered trees.
  • Computational experiments were performed on artificial tree datasets.

Main Results:

  • The study successfully formulated and implemented integer programming models for minimum CFG, EOTG, and EUTG discovery.
  • Experimental validation was conducted on both ordered and unordered tree structures.
  • The efficacy of the developed methods was demonstrated through application to glycan tree structure pattern extraction.

Conclusions:

  • Integer programming provides an effective framework for deriving minimal CFGs, EOTGs, and EUTGs.
  • The proposed tree grammar methods are valuable tools for identifying patterns in glycan structures.