Fast Discovery of Reliable Subnetworks

Peer reviewe

TreeDT : tree pattern mining for gene mapping

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.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.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.Peer reviewe

Hiki, ähky ja loikka - Osallistujien pedagogisia mietteitä ja ideoita hankkeen varrelta

DIGIJOUJOU-hankkeessa työskennelleet opettajat ovat hankkeen toimintavuosien 2017-2019 aikana pohtineet opetuksen ja oppimisen digitaalisuutta ja joustavuutta eri näkökulmista: mitä digitaalisuus ja joustavuus suomen ja ruotsin opiskelussa tarkoittaa, miten soveltaa, lisätä ja kehittää digitaalisuutta ja joustavuutta omassa opetuksessa ja opiskelijoiden oppimisessa. Hankelaisten blogikirjoituksissa näemme askeleita opettajien omasta ja yhdessä muiden kanssa oppimisesta hankkeen edetessä; epävarmuus muuttuu varmuudeksi, ajoittainen digiähky oman asiantuntijuuden kasvuksi ja joustavuus osaksi opettajan arkipedagogiikkaa. Antoisia ja inspiroivia lukuhetkiä! Lisätietoa: https://digijoujou.aalto.fi/Lärarna i DIGIJOUJOU-projektet har under projektets verksamhetsår 2017-2019 reflekterat över digitalisering och exibilitet från olika perspektiv; vad betyder digitalisering och exibilitet i lärandet av finska och svenska, hur ska man implementera, öka och utveckla dessa i den egna undervisningen och i hur studerande lär sig finska och svenska. I projektdeltagarnas bloginlägg får vi inblick i hur allas lärandeprocess i projektet framskrider; osäkerhet utvecklas till säkerhet, digikaoset får ordning och exibilitet blir en del av den egna sakkunnigheten och pedagogiken. Med önskan om givande och inspirerande läsning! Mer information: https://digijoujou.aalto.fi

Subgraph Queries by Context-free Grammars

We describe a method for querying vertex- and edge-labeled graphs using context-free grammars to specify the class of interesting paths. We introduce a novel problem, finding the connection subgraph induced by the set of matching paths between given two vertices or two sets of vertices. Such a subgraph provides a concise summary of the relationship between the vertices. We also present novel algorithms for parsing subgraphs directly without enumerating all the individual paths. We evaluate experimentally the presented parsing algorithms on a set of real graphs derived from publicly available biomedical databases and on randomly generated graphs. The results indicate that parsing the connection subgraph directly is much more effective than parsing individual paths separately. Furthermore, we show that using a bidirectional parsing algorithm, in most cases, allows for searching twice as long paths as using a unidirectional search strategy

174 IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, VOL. 3, NO. 2, APRIL-JUNE 2006 TreeDT: Tree Pattern Mining for Gene Mapping

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. Index Terms—Biology and genetics, nonparametric statistics, nonnumerical algorithms and problems.

Gene Mapping by Haplotype Pattern Mining Hannu T.T. Toivonen1

Abstract Genetic markers are being increasingly utilized in genemapping. Discovery of associations between markers and patient phenotypes-- such as a disease status-- enablesthe identification of potential disease gene loci. The rationale is that in diseases with a reasonable genetic contri-bution, diseased individuals are more likely to have associated marker alleles near the disease susceptibility genethan control individuals. We describe a new gene mapping method, HaplotypePattern Mining (HPM), that is based on discovering recurrent marker patterns. We define a class of useful haplotypepatterns in genetic case-control data, give an algorithm for finding disease-associated haplotypes, and show how to usethem to identify disease susceptibility loci. Experimental studies show that the method has good lo-calization power in data sets with large degrees of phenocopies and with lots of missing and erroneous data. Wealso demonstrate how the method can be used to discover several genes simultaneously. 1

Data mining for gene mapping

Localization of disease susceptibility genes to certain areas in the human genome, or gene mapping, requires careful analysis of genetic marker data. Gene mapping is often carried out using a sample of individuals affected by the disease of interest and a sample of healthy controls. From a data mining perspective, gene mapping can then be cast as a pattern discov-ery and analysis task: which genetically motivated marker patterns help to separate affected individuals from healthy ones? The marker data constitutes haplotypes: a haplotype is a string of genetic markers from one chromosome. Individuals who share a common ancestor, such as those that have inherited the disease gene from this individual, potentially share a substring in their haplotypes. Classi-fication or association analysis of haplotypes is thus one approach to gene mapping. Further, analyzing the similarities of haplotypes and clustering them can provide insight to genetic rela-tionships of individuals, to different mutations, and thus to the genetic etiology of the disease. We describe and illustrate data mining approaches to gene mapping using haplotypes: as-sociation analysis, similarity analysis, and clustering. The association-based gene mapping methods have been found to perform well and are being routinely applied in gene mapping projects