An iterative algorithm for tagSNP selection based on information entropy analysis

Chia Hung Yeh*, Jing Wun Jheng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

In this research, an iterative algorithm based on information entropy analysis is proposed for tagSNPs selection. Dynamic programming algorithm is employed to partition haplotypes into blocks with the first constraint, the minimum total block entropy. Missing SNPs are inferred with the second constraint, the minimum number of tagSNPs. The proposed algorithm iterates between these two phases with the above two constraints until the number of tagSNPs reaches its minimum. The proposed algorithm is simulated with two data sets, including Daly et al. 2001, and Patil et al. 2001. Experimental results show that the proposed scheme reduces the number of tagSNPs required in haplotypes significantly.

Original languageEnglish
Pages (from-to)233-239
Number of pages7
JournalJournal of Signal Processing Systems
Volume64
Issue number2
DOIs
Publication statusPublished - 2011 Aug
Externally publishedYes

Keywords

  • Bioinformatics
  • Entropy
  • Haplotype
  • Information
  • Single nucleotide polymorphisms
  • tagSNP

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Signal Processing
  • Information Systems
  • Modelling and Simulation
  • Hardware and Architecture

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