The Haplotyping problem: an overview of computational models and solutions
Journal of Computer Science and Technology
Integer Programming Approaches to Haplotype Inference by Pure Parsimony
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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
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
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A haplotype is a DNA sequence that is inherited from one parent. They are especially important in the study of complex diseases since they contain more information than genotype data, so the next high priority phase in human genomics involves the development of a full Haplotype Map of human genome [1]. However, obtaining haplotype data is technically difficult and expensive. One of the computational methods for obtaining haplotype data from genotype data is the pure parsimony criterion, an approach known as Haplotype Inference by Pure Parsimony (HIPP). It has been proved to be an NP-hard problem. We present a new preprocessing method which drastically decreases the number of relevant haplotypes. Several algorithms need to preprocess data; for big problem instances this key procedure is even more important than the process. This preprocessing was eventually tested on real and simulated data applying a tabu search, and the performance of the resulting algorithm showed it to be competitive with the best actual solvers.