Vesa Ollikainen

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We introduce a new method for linkage disequilibrium mapping: haplotype pattern mining (HPM). The method, inspired by data mining methods, is based on discovery of recurrent patterns. We define a class of useful haplotype patterns in genetic case-control data and use the algorithm for finding disease-associated haplotypes. The haplotypes are ordered by(More)
Developmental dyslexia is a neurofunctional disorder characterised by an unexpected difficulty in learning to read and write despite adequate intelligence, motivation, and education. Previous studies have suggested mostly quantitative susceptibility loci for dyslexia on chromosomes 1, 2, 6, and 15, but no genes have been identified yet. We studied a large(More)
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)
Previously, we have presented a data mining-based algorithmic approach to genetic association analysis, Haplotype Pattern Mining. We have now extended the approach with the possibility of analysing quantitative traits and utilising covariates. This is accomplished by using a linear model for measuring association. We present results with the extended(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 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)
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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