An iterative algorithm for tagSNP selection based on information entropy analysis

Chia Hung Yeh; Jing Wun Jheng

doi:10.1007/s11265-009-0440-6

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 journal › Article › peer-review

1 Citation (Scopus)

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 language	English
Pages (from-to)	233-239
Number of pages	7
Journal	Journal of Signal Processing Systems
Volume	64
Issue number	2
DOIs	https://doi.org/10.1007/s11265-009-0440-6
Publication status	Published - 2011 Aug
Externally published	Yes

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

Access to Document

10.1007/s11265-009-0440-6

Cite this

@article{d0c6f917218343a69908ef19ecaf0f4f,

title = "An iterative algorithm for tagSNP selection based on information entropy analysis",

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.",

keywords = "Bioinformatics, Entropy, Haplotype, Information, Single nucleotide polymorphisms, tagSNP",

author = "Yeh, {Chia Hung} and Jheng, {Jing Wun}",

year = "2011",

month = aug,

doi = "10.1007/s11265-009-0440-6",

language = "English",

volume = "64",

pages = "233--239",

journal = "Journal of Signal Processing Systems",

issn = "1939-8018",

publisher = "Springer New York",

number = "2",

}

TY - JOUR

T1 - An iterative algorithm for tagSNP selection based on information entropy analysis

AU - Yeh, Chia Hung

AU - Jheng, Jing Wun

PY - 2011/8

Y1 - 2011/8

N2 - 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.

AB - 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.

KW - Bioinformatics

KW - Entropy

KW - Haplotype

KW - Information

KW - Single nucleotide polymorphisms

KW - tagSNP

UR - http://www.scopus.com/inward/record.url?scp=80051670019&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80051670019&partnerID=8YFLogxK

U2 - 10.1007/s11265-009-0440-6

DO - 10.1007/s11265-009-0440-6

M3 - Article

AN - SCOPUS:80051670019

SN - 1939-8018

VL - 64

SP - 233

EP - 239

JO - Journal of Signal Processing Systems

JF - Journal of Signal Processing Systems

IS - 2

ER -

An iterative algorithm for tagSNP selection based on information entropy analysis

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this