Complexity and Approximation: Combinatorial Optimization Problems and Their Approximability Properties
Haplotyping as perfect phylogeny: conceptual framework and efficient solutions
Proceedings of the sixth annual international conference on Computational biology
A Practical Algorithm for Optimal Inference of Haplotypes from Diploid Populations
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
The Haplotyping problem: an overview of computational models and solutions
Journal of Computer Science and Technology
Opportunities for Combinatorial Optimization in Computational Biology
INFORMS Journal on Computing
An approximation algorithm for haplotype inference by maximum parsimony
Proceedings of the 2005 ACM symposium on Applied computing
Haplotyping Populations by Pure Parsimony: Complexity of Exact and Approximation Algorithms
INFORMS Journal on Computing
Haplotype inference by pure Parsimony
CPM'03 Proceedings of the 14th annual conference on Combinatorial pattern matching
A linear-time algorithm for the perfect phylogeny haplotyping (PPH) problem
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
A polynomial case of the parsimony haplotyping problem
Operations Research Letters
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Combinatorial haplotyping problems have received great attention in the past few years. We review their definitions and the main results that were obtained for their solution. Haplotyping problems require one to determine a set H of binary vectors (called haplotypes) that explain a set of G of ternary vectors (called genotypes). The number @g(G) of haplotypes to choose from can be exponential with respect to the number of genotypes. We give an exact formula, based on the inclusion-exclusion principle, for determining @g(G).