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Single Cell Multiplex Reverse Transcription Polymerase Chain Reaction After Patch-clamp
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Published on: June 20, 2018

Frequent patterns mining in multiple biological sequences.

Ling Chen1, Wei Liu

  • 1College of Information Engineering, Yangzhou University, Yangzhou, Jiangsu 225009, China; National Key Lab of Novel Software Tech, Nanjing University, Nanjing 210093, China.

Computers in Biology and Medicine
|September 17, 2013
PubMed
Summary
This summary is machine-generated.

A new algorithm, Multiple Biological Sequence Pattern Mining (MSPM), efficiently finds frequent patterns in biological sequences. It reduces computation time and memory usage, offering faster speeds and higher quality results than existing methods.

Keywords:
Biological sequenceFrequent pattern miningPrefix treePrimary pattern

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Mining

Background:

  • Mining frequent patterns in multiple biological sequences is computationally intensive.
  • Existing algorithms often create large intermediate datasets and numerous candidate patterns, increasing resource demands.

Purpose of the Study:

  • To develop a fast and efficient algorithm for mining frequent patterns in multiple biological sequences.
  • To address the limitations of existing methods regarding computation time and memory requirements.

Main Methods:

  • Introduced the concept of a primary pattern, a foundational unit for pattern extension.
  • Utilized a prefix tree to efficiently detect frequent primary patterns.
  • Employed a pattern-extending approach to mine larger frequent patterns without generating excessive irrelevant candidates.

Main Results:

  • The proposed Multiple Biological Sequence Pattern Mining (MSPM) algorithm demonstrated superior performance.
  • MSPM achieved significantly faster execution speeds compared to existing algorithms.
  • The algorithm yielded higher quality results, indicating more relevant pattern discovery.

Conclusions:

  • The MSPM algorithm offers an effective solution for frequent pattern mining in multiple biological sequences.
  • This approach optimizes computational efficiency and enhances the accuracy of pattern discovery in bioinformatics.
  • MSPM represents an advancement in analyzing complex biological sequence data.