TreeDT : tree pattern mining for gene mapping

Show simple item record

dc.contributor.author Sevon, Petteri
dc.contributor.author Toivonen, Hannu
dc.contributor.author Ollikainen, Vesa
dc.date.accessioned 2014-11-17T12:39:00Z
dc.date.available 2014-11-17T12:39:00Z
dc.date.issued 2006
dc.identifier.citation Sevon , P , Toivonen , H & Ollikainen , V 2006 , ' TreeDT : tree pattern mining for gene mapping ' , IEEE/ACM Transactions on Computational Biology and Bioinformatics , vol. 3 , no. 2 , pp. 174-185 .
dc.identifier.other PURE: 588666
dc.identifier.other PURE UUID: 5a1510cf-b452-4315-9026-c30a318d50f9
dc.identifier.other dawa_publication: 154506
dc.identifier.other WOS: 000237260400007
dc.identifier.other ORCID: /0000-0003-1339-8022/work/29478258
dc.identifier.other Scopus: 33645226692
dc.identifier.uri http://hdl.handle.net/10138/143994
dc.description.abstract 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 constructed at a given chromosomal location is an estimate of the genealogy of the haplotypes. TreeDT constructs these trees for all locations on the given haplotypes and performs a novel disequilibrium test on each tree: Is there a small set of subtrees with relatively high proportions of disease-associated chromosomes, suggesting shared genetic history for those and a likely disease gene location? We give a detailed description of TreeDT and the tree disequilibrium tests, we analyze the algorithm formally, and we evaluate its performance experimentally on both simulated and real data sets. Experimental results demonstrate that TreeDT has high accuracy on difficult mapping tasks and comparisons to other methods (EATDT, HPM, TDT) show that TreeDT is very competitive. sv
dc.description.abstract 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 constructed at a given chromosomal location is an estimate of the genealogy of the haplotypes. TreeDT constructs these trees for all locations on the given haplotypes and performs a novel disequilibrium test on each tree: Is there a small set of subtrees with relatively high proportions of disease-associated chromosomes, suggesting shared genetic history for those and a likely disease gene location? We give a detailed description of TreeDT and the tree disequilibrium tests, we analyze the algorithm formally, and we evaluate its performance experimentally on both simulated and real data sets. Experimental results demonstrate that TreeDT has high accuracy on difficult mapping tasks and comparisons to other methods (EATDT, HPM, TDT) show that TreeDT is very competitive. fi
dc.description.abstract 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 constructed at a given chromosomal location is an estimate of the genealogy of the haplotypes. TreeDT constructs these trees for all locations on the given haplotypes and performs a novel disequilibrium test on each tree: Is there a small set of subtrees with relatively high proportions of disease-associated chromosomes, suggesting shared genetic history for those and a likely disease gene location? We give a detailed description of TreeDT and the tree disequilibrium tests, we analyze the algorithm formally, and we evaluate its performance experimentally on both simulated and real data sets. Experimental results demonstrate that TreeDT has high accuracy on difficult mapping tasks and comparisons to other methods (EATDT, HPM, TDT) show that TreeDT is very competitive. en
dc.format.extent 12
dc.language.iso eng
dc.relation.ispartof IEEE/ACM Transactions on Computational Biology and Bioinformatics
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject 113 Computer and information sciences
dc.title TreeDT : tree pattern mining for gene mapping en
dc.type Article
dc.contributor.organization Department of Computer Science
dc.contributor.organization Discovery Research Group/Prof. Hannu Toivonen
dc.description.reviewstatus Peer reviewed
dc.relation.issn 1545-5963
dc.rights.accesslevel openAccess
dc.type.version publishedVersion

Files in this item

Total number of downloads: Loading...

Files Size Format View
treedt_tcbb_06.pdf 1.269Mb PDF View/Open

This item appears in the following Collection(s)

Show simple item record