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Published on: June 20, 2018

PMBC: pattern mining from biological sequences with wildcard constraints.

Xindong Wu¹, Xingquan Zhu, Yu He

¹Department of Computer Science, University of Vermont, Burlington, VT 05401, USA. xwu@cs.uvm.edu

Computers in Biology and Medicine

|April 10, 2013

Summary

This summary is machine-generated.

This study introduces PMBC, a novel method for discovering frequent patterns in biological sequences without needing user-defined gap constraints. PMBC efficiently identifies patterns and their frequencies, aiding biological data analysis.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Biological sequences contain complex patterns crucial for domain experts.
Existing pattern mining tools require manual gap constraint specification, which is time-consuming and non-intuitive.
Variations in pattern occurrences necessitate flexible pattern discovery methods.

Purpose of the Study:

To develop an automated and efficient method for frequent pattern mining in biological sequences without user-specified gap constraints.
To introduce Pattern Mining from Biological sequences with wildcard Constraints (PMBC) for discovering subsequences meeting a given support threshold.
To address the limitations of existing tools regarding gap constraint definition and its impact on pattern mining.

Main Methods:

Proposed PMBC algorithm for frequent pattern mining in biological sequences.
Utilized two heuristic scanning methods (one-way and two-way) for subsequence discovery and frequency estimation.
Evaluated performance using synthetic and real-world DNA sequence datasets.

Main Results:

PMBC successfully discovers frequent subsequences and estimates their frequencies without user-defined gap constraints.
The proposed heuristic methods demonstrate effective performance in pattern mining and frequency estimation.
Experimental results validate the efficiency and applicability of PMBC on diverse biological sequence data.

Conclusions:

PMBC offers an automated and efficient solution for frequent pattern mining in biological sequences, overcoming limitations of existing methods.
The developed approach facilitates the discovery of biologically relevant patterns with inherent variations.
This work contributes to advancing pattern discovery techniques in bioinformatics and computational biology.