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Mining frequent biological sequences based on bitmap without candidate sequence generation.

Qian Wang1, Darryl N Davis2, Jiadong Ren3

  • 1College of Information Science and Engineering, Yanshan University, Qianhuangdao, Hebei, China; Computer Virtual Technology and System Integration Laboratory of Hebei Province, China; Department of Computer Science, University of Hull, Hull, UK.

Computers in Biology and Medicine
|January 17, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces FBSB, an efficient algorithm for mining frequent biological sequences using bitmaps. FBSB improves accuracy and performance for genetic information analysis and disease prediction.

Keywords:
Biological sequenceBitmapFrequent patternQuicksort list

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Biological sequences contain crucial genetic information, vital for understanding organism inheritance and protein structure-function relationships.
  • Accurate analysis of biological sequences aids in applications like disease prediction.
  • Existing frequent sequence mining algorithms struggle with the unique characteristics of biological data, leading to inefficiency and errors.

Purpose of the Study:

  • To propose an efficient and accurate algorithm for mining frequent biological sequences.
  • To address the limitations of current algorithms in handling biological sequence data.

Main Methods:

  • Introduced the Frequent Biological Sequence based on Bitmap (FBSB) algorithm.
  • Utilized bitmaps as a simple data structure.
  • Transformed rows into Quicksort lists (QS-lists) for sequence growth.
  • Employed real biological sequences for testing to ensure continuity and accuracy.

Main Results:

  • FBSB demonstrated superior performance compared to existing algorithms in terms of runtime and scalability.
  • The algorithm successfully mined all frequent sequences without errors.
  • FBSB maintains accuracy and continuity requirements for biological sequence mining.

Conclusions:

  • FBSB offers a significant improvement for frequent biological sequence mining.
  • The algorithm's efficiency and accuracy make it suitable for genetic information analysis and disease prediction applications.
  • FBSB provides a robust solution for handling complex biological sequence data.