Vesa Ollikainen

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We describe TreeDT, a novel association-based gene mapping method. Given a set of disease-associated haplotypes and a set of control haplotypes, TreeDT predicts likely locations of a disease susceptibility gene. TreeDT extracts, essentially in the form of haplotype trees, information about historical recombinations in the population: A haplotype tree(More)
We describe a new method for linkage disequilibrium mapping, Haplotype Pattern Mining (HPM). The method is based on discovering recurrent patterns, inspired by data mining methods. We define a class of useful haplotype patterns in genetic case-control data, and give an algorithm for finding disease-associated haplotypes. The haplotypes are ordered by their(More)
Sequence similarity/database searching is a cornerstone of molecular biology. PairsDB is a database intended to make exploring protein sequences and their similarity relationships quick and easy. Behind PairsDB is a comprehensive collection of protein sequences and BLAST and PSI-BLAST alignments between them. Instead of running BLAST or PSI-BLAST(More)
We introduce and evaluate TreeDT, a novel gene mapping method which is based on discovering and assessing tree-like patterns in genetic marker data. Gene mapping aims at discovering a statistical connection from a particular disease or trait to a narrow region in the genome. In a typical case-control setting, data consists of genetic markers typed for a set(More)
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