Haplotyping Problem, A Clustering Approach

Journal Article · Thu Sep 06 00:00:00 EDT 2007 · AIP Conference Proceedings

DOI:https://doi.org/10.1063/1.2790104· OSTI ID:21035858

Eslahchi, Changiz ^[1]; Sadeghi, Mehdi ^[2]; Pezeshk, Hamid ^[3]; Kargar, Mehdi ^[4]; Poormohammadi, Hadi ^[1]

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1. Faculty of Mathematics, Shahid-Beheshti University, Tehran (Iran, Islamic Republic of)
2. National Institute for Genetic Engineering and Biotechnology, Tehran-Karaj Highway, Tehran (Iran, Islamic Republic of)
3. Center of Excellence in Biomathematics, School of Mathematics, Statistics and Computer Science, University College of Science, University of Tehran, Tehran (Iran, Islamic Republic of)
4. Department of Computer Engineering, Sharif University of Technology, Tehran (Iran, Islamic Republic of)

Construction of two haplotypes from a set of Single Nucleotide Polymorphism (SNP) fragments is called haplotype reconstruction problem. One of the most popular computational model for this problem is Minimum Error Correction (MEC). Since MEC is an NP-hard problem, here we propose a novel heuristic algorithm based on clustering analysis in data mining for haplotype reconstruction problem. Based on hamming distance and similarity between two fragments, our iterative algorithm produces two clusters of fragments; then, in each iteration, the algorithm assigns a fragment to one of the clusters. Our results suggest that the algorithm has less reconstruction error rate in comparison with other algorithms.

• OSTI ID: 21035858
• Journal Information: AIP Conference Proceedings, Vol. 936, Issue 1; Conference: International conference on numerical analysis and applied mathematics, Corfu (Greece), 16-20 Sep 2007; Other Information: DOI: 10.1063/1.2790104; (c) 2007 American Institute of Physics; Country of input: International Atomic Energy Agency (IAEA); ISSN 0094-243X
• Country of Publication: United States
• Language: English

Related Subjects

71 CLASSICAL AND QUANTUM MECHANICS
GENERAL PHYSICS
ALGORITHMS
COMPARATIVE EVALUATIONS
CORRECTIONS
ERRORS
ITERATIVE METHODS
NUCLEOTIDES
PATTERN RECOGNITION