Extending and implementing the stable model semantics
Artificial Intelligence
Knowledge Representation, Reasoning, and Declarative Problem Solving
Knowledge Representation, Reasoning, and Declarative Problem Solving
Integer Programming Approaches to Haplotype Inference by Pure Parsimony
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Answer Set Programming Based on Propositional Satisfiability
Journal of Automated Reasoning
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
Conflict-driven answer set solving
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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
Metabolic Network Expansion with Answer Set Programming
ICLP '09 Proceedings of the 25th International Conference on Logic Programming
HAPLO-ASP: Haplotype Inference Using Answer Set Programming
LPNMR '09 Proceedings of the 10th International Conference on Logic Programming and Nonmonotonic Reasoning
Present and Future Challenges for ASP Systems
LPNMR '09 Proceedings of the 10th International Conference on Logic Programming and Nonmonotonic Reasoning
Potassco: The Potsdam Answer Set Solving Collection
AI Communications - Answer Set Programming
Challenges in answer set solving
Logic programming, knowledge representation, and nonmonotonic reasoning
Applications of answer set programming in phylogenetic systematics
Logic programming, knowledge representation, and nonmonotonic reasoning
A constraint solver for flexible protein models
Journal of Artificial Intelligence Research
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Identifying maternal and paternal inheritance is essential to be able to find the set of genes responsible for a particular disease. However, due to technological limitations, we have access to genotype data (genetic makeup of an individual), and determining haplotypes (genetic makeup of the parents) experimentally is a costly and time consuming procedure. With these biological motivations, we study a computational problem, called Haplotype Inference by Pure Parsimony (HIPP), that asks for the minimal number of haplotypes that form a given set of genotypes. HIPP has been studied using integer linear programming, branch and bound algorithms, SAT-based algorithms, or pseudo-boolean optimization methods. We introduce a new approach to solving HIPP, using Answer Set Programming (ASP). According to our experiments with a large number of problem instances (some automatically generated and some real), our ASP-based approach solves the most number of problems compared with other approaches. Due to the expressivity of the knowledge representation language of ASP, our approach allows us to solve variations of HIPP, e.g., with additional domain specific information, such as patterns/parts of haplotypes observed for some gene family, or with some missing genotype information. In this sense, the ASP-based approach is more general than the existing approaches to haplotype inference.