Global Constraints for Lexicographic Orderings
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Perfect phylogeny and haplotype assignment
RECOMB '04 Proceedings of the eighth annual international conference on Resaerch in computational molecular biology
Handbook of Computational Molecular Biology (Chapman & All/Crc Computer and Information Science Series)
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
Boosting Haplotype Inference with Local Search
Constraints
Two-Level ACO for Haplotype Inference Under Pure Parsimony
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
Mendelian error detection in complex pedigrees using weighted constraint satisfaction techniques
Proceedings of the 2007 conference on Artificial Intelligence Research and Development
Efficiently Calculating Evolutionary Tree Measures Using SAT
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
HAPLO-ASP: Haplotype Inference Using Answer Set Programming
LPNMR '09 Proceedings of the 10th International Conference on Logic Programming and Nonmonotonic Reasoning
Efficient haplotype inference with answer set programming
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Efficient haplotype inference with answer set programming
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
A new preprocessing procedure for the haplotype inference problem
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Breaking symmetries in SAT matrix models
SAT'07 Proceedings of the 10th international conference on Theory and applications of satisfiability testing
Efficient haplotype inference with pseudo-boolean optimization
AB'07 Proceedings of the 2nd international conference on Algebraic biology
Faster phylogenetic inference with MXG
LPAR'07 Proceedings of the 14th international conference on Logic for programming, artificial intelligence and reasoning
EPIA'07 Proceedings of the aritficial intelligence 13th Portuguese conference on Progress in artificial intelligence
Efficient haplotype inference with combined CP and OR techniques
CPAIOR'08 Proceedings of the 5th international conference on Integration of AI and OR techniques in constraint programming for combinatorial optimization problems
Combinatorial Optimization Solutions for the Maximum Quartet Consistency Problem
Fundamenta Informaticae - RCRA 2008 Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion
An effective algorithm for and phase transitions of the directed hamiltonian cycle problem
Journal of Artificial Intelligence Research
Grounding formulas with complex terms
Canadian AI'11 Proceedings of the 24th Canadian conference on Advances in artificial intelligence
A SAT based effective algorithm for the directed hamiltonian cycle problem
CSR'10 Proceedings of the 5th international conference on Computer Science: theory and Applications
SAT in bioinformatics: making the case with haplotype inference
SAT'06 Proceedings of the 9th international conference on Theory and Applications of Satisfiability Testing
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One of the main topics of research in genornics is determining the relevance of mutations, described in haplotype data, as causes of some genetic diseases. However, due to technological limitations, genotype data rather than haplotype data is usually obtained. The haplotype inference by pure parsimony (HIPP) problem consists in inferring haplotypes from genotypes S.t. the number of required haplotypes is minimum. Previous approaches to the HIPP problem have focused on integer programming models and branch-and-bound algorithms. In contrast, this paper proposes the utilization of Boolean Satisfiability (SAT). The proposed solution entails a SAT model, a number of key pruning techniques, and an iterative algorithm that enumerates the possible solution values for the target optimization problem. Experimental results, obtained on a wide range of instances, demonstrate that the SAT-based approach can be several orders of magnitude faster than existing solutions. Besides being more efficient, the SAT-based approach is also the only capable of computing the solution for a large number of instances.