| Abstract: |  This work deals with the problem of haplotype frequency prediction and haplotype resolution using statistical methods in general, and specifically in the context of HLA data. When performing a search for unrelated hematopoietic stem cell transplantation donors, efficient haplotype resolution of individuals in the world registers of potential bone marrow donors can be of great benefit. I have proposed a new Bayesian approach that uses the available prior knowledge to solve this task. My algorithm is compared with an existing EM algorithm developed in the BioDat group at the Czech Technical University, and even though it gives worse results in terms of accuracy, its robustness in speed when faced with large datasets with missing or ambiguous information in principle allows for processing of register data on a massive scale.
|
|---|