Integer Programming Approaches to Haplotype Inference by Pure Parsimony
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Haplotyping Populations by Pure Parsimony: Complexity of Exact and Approximation Algorithms
INFORMS Journal on Computing
Boosting Haplotype Inference with Local Search
Constraints
Efficient haplotype inference with boolean satisfiability
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Haplotype inference by pure Parsimony
CPM'03 Proceedings of the 14th annual conference on Combinatorial pattern matching
Efficient haplotype inference with pseudo-boolean optimization
AB'07 Proceedings of the 2nd international conference on Algebraic biology
A new preprocessing procedure for the haplotype inference problem
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Efficient and accurate haplotype inference by combining parsimony and pedigree information
ANB'10 Proceedings of the 4th international conference on Algebraic and Numeric Biology
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Haplotype inference has relevant biological applications, and represents a challenging computational problem. Among others, pure parsimony provides a viable modeling approach for haplotype inference and provides a simple optimization criterion. Alternative approaches have been proposed for haplotype inference by pure parsimony (HIPP), including branch and bound, integer programming and, more recently, propositional satisfiability and pseudo-Boolean optimization (PBO). Among these, the currently best performing HIPP approach is based on PBO. This paper proposes a number of effective improvements to PBO-based HIPP, including the use of lower bounding and pruning techniques effective with other approaches. The new PBO-based HIPP approach reduces by 50% the number of instances that remain unsolvable by HIPP based approaches.