Haplotyping as perfect phylogeny: conceptual framework and efficient solutions
Proceedings of the sixth annual international conference on Computational biology
Haplotypes and informative SNP selection algorithms: don't block out information
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
Large scale reconstruction of haplotypes from genotype data
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
Model-based inference of haplotype block variation
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
Bayesian multi-population haplotype inference via a hierarchical dirichlet process mixture
ICML '06 Proceedings of the 23rd international conference on Machine learning
A fast haplotype inference method for large population genotype data
Computational Statistics & Data Analysis
Algorithm for haplotype resolution and block partitioning for partial XOR-genotype data
Journal of Biomedical Informatics
Phylogenetic network inferences through efficient haplotyping
WABI'06 Proceedings of the 6th international conference on Algorithms in Bioinformatics
A hidden markov technique for haplotype reconstruction
WABI'05 Proceedings of the 5th International conference on Algorithms in Bioinformatics
HAPLOFREQ: estimating haplotype frequencies efficiently
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
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We present a new algorithm for the problems of genotype phasing and block partitioning. Our algorithm is based on a new stochastic model, and on the novel concept of probabilistic common haplotypes. We formulate the goals of genotype resolving and block partitioning as a maximum likelihood problem, and solve it by an EM algorithm. When applied to real biological SNP data, our algorithm outperforms two state of the art phasing algorithms. Our algorithm is also considerably more sensitive and accurate than a previous method in predicting and identifying disease association.