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Novel Sequence Discovery by Subtractive Genomics
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A New Approach for Mining Order-Preserving Submatrices Based on All Common Subsequences.

Yun Xue1, Zhengling Liao1, Meihang Li1

  • 1Laboratory of Quantum Engineering and Quantum Materials, School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou 510006, China.

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This study introduces an exact method for discovering all order-preserving submatrices (OPSMs) using frequent sequential pattern mining. The approach efficiently identifies deep OPSMs, overcoming limitations of existing heuristic algorithms.

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Area of Science:

  • Bioinformatics
  • Machine Learning
  • Data Mining

Background:

  • Order-preserving submatrices (OPSMs) are crucial unsupervised learning models in fields like DNA analysis and recommendation systems.
  • Existing heuristic methods struggle with the NP-complete problem of discovering all OPSMs, especially deep OPSMs, due to high computational costs.
  • Deep OPSMs, characterized by long patterns with sparse support, are often missed by current popular algorithms.

Purpose of the Study:

  • To propose an exact and efficient method for discovering all order-preserving submatrices (OPSMs).
  • To address the computational challenges and limitations of existing heuristic algorithms in identifying deep OPSMs.
  • To develop a method capable of finding all common subsequences (ACS) and mining frequent sequential patterns for OPSM discovery.

Main Methods:

  • An existing algorithm was adapted to find all common subsequences (ACS) between pairs of row sequences.
  • An improved prefix tree data structure was utilized for efficient storage and traversal of ACS.
  • The Apriori principle was applied to mine frequent sequential patterns, enabling the discovery of all OPSMs.
  • Experiments were conducted on both gene and synthetic datasets to validate the method.

Main Results:

  • The proposed method successfully identified all deep OPSMs, which were previously pruned by other algorithms.
  • Experimental results demonstrated the effectiveness of the approach in discovering OPSMs.
  • The method proved to be efficient in terms of computational performance on the tested datasets.

Conclusions:

  • The developed exact method based on frequent sequential pattern mining effectively discovers all OPSMs, including deep OPSMs.
  • This approach overcomes the limitations of heuristic algorithms and offers improved efficiency and effectiveness.
  • The findings have significant implications for applications requiring comprehensive OPSM identification, such as in bioinformatics and recommendation systems.