Parameterized Complexity Theory (Texts in Theoretical Computer Science. An EATCS Series)
Parameterized Complexity Theory (Texts in Theoretical Computer Science. An EATCS Series)
Islands of Tractability for Parsimony Haplotyping
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Invitation to data reduction and problem kernelization
ACM SIGACT News
Haplotyping Populations by Pure Parsimony: Complexity of Exact and Approximation Algorithms
INFORMS Journal on Computing
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Infeasibility of instance compression and succinct PCPs for NP
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Haplotype Inference Constrained by Plausible Haplotype Data
CPM '09 Proceedings of the 20th Annual Symposium on Combinatorial Pattern Matching
On problems without polynomial kernels
Journal of Computer and System Sciences
A polynomial case of the parsimony haplotyping problem
Operations Research Letters
Haplotype Inference Constrained by Plausible Haplotype Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Phylogeny- and parsimony-based haplotype inference with constraints
Information and Computation
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Parsimony haplotyping is the problem of finding a smallest size set of haplotypes that can explain a given set of genotypes. The problem is NP-hard, and many heuristic and approximation algorithms as well as polynomial-time solvable special cases have been discovered. We propose improved fixed-parameter tractability results with respect to the parameter "size of the target haplotype set" k by presenting an O*(k4k)-time algorithm. This also applies to the practically important constrained case, where we can only use haplotypes from a given set. Furthermore, we show that the problem becomes polynomial-time solvable if the given set of genotypes is complete, i.e., contains all possible genotypes that can be explained by the set of haplotypes.