Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
The EM algorithm for graphical association models with missing data
Computational Statistics & Data Analysis - Special issue dedicated to Toma´sˇ Havra´nek
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Bucket elimination: a unifying framework for probabilistic inference
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Maximum likelihood resolution of multi-block genotypes
RECOMB '04 Proceedings of the eighth annual international conference on Resaerch in computational molecular biology
Bayesian haplo-type inference via the dirichlet process
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Hierarchical Decision Tree Induction in Distributed Genomic Databases
IEEE Transactions on Knowledge and Data Engineering
Solving haplotyping inference parsimony problem using a new basic polynomial formulation
Computers & Mathematics with Applications
A fast haplotype inference method for large population genotype data
Computational Statistics & Data Analysis
Efficient Algorithms for SNP Haplotype Block Selection Problems
COCOON '08 Proceedings of the 14th annual international conference on Computing and Combinatorics
Haplotype Inference Constrained by Plausible Haplotype Data
CPM '09 Proceedings of the 20th Annual Symposium on Combinatorial Pattern Matching
Algorithm for haplotype resolution and block partitioning for partial XOR-genotype data
Journal of Biomedical Informatics
Haplotype Inference Constrained by Plausible Haplotype Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A hidden markov technique for haplotype reconstruction
WABI'05 Proceedings of the 5th International conference on Algorithms in Bioinformatics
| Hi-index | 0.00 |
The uneven recombination structure of human DNA has been highlighted by several recent studies. Knowledge of the haplotype blocks generated by this phenomenon can be applied to dramatically increase the statistical power of genetic mapping. Several criteria have already been proposed for identifying these blocks, all of which require haplotypes as input. We propose a comprehensive statistical model of haplotype block variation and show how the parameters of this model can be learned from haplotypes and/or unphased genotype data. Using real-world SNP data, we demonstrate that our approach can be used to resolve genotypes into their constituent haplotypes with greater accuracy than previously known methods.