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dc.contributor.authorJin, Yingen_US
dc.date.accessioned2014-03-14T21:23:44Z
dc.date.available2014-03-14T21:23:44Z
dc.date.issued2009-12-08en_US
dc.identifier.otheretd-12302009-142944en_US
dc.identifier.urihttp://hdl.handle.net/10919/40493
dc.description.abstractBiological network data is plentiful with practically every experimental methodology giving â network viewsâ into cellular function and behavior. Bioinformatic screens that yield network data include, for example, genome-wide deletion screens, protein-protein interaction assays, RNA interference experiments, and methods to probe metabolic pathways. Efficient and comprehensive computational approaches are required to model these screens and gain insight into the nature of biological networks. This thesis presents three new algorithms to model and mine network datasets. First, we present an algorithm that models genome-wide perturbation screens by deriving relations between phenotypes and subsequently using these relations in a local manner to derive genephenotype relationships. We show how this algorithm outperforms all previously described algorithms for gene-phenotype modeling. We also present theoretical insight into the convergence and accuracy properties of this approach. Second, we define a new data mining problemâ constrained minimal separator miningâ and propose algorithms as well as applications to modeling gene perturbation screens by viewing the perturbed genes as a graph separator. Both of these data mining applications are evaluated on network datasets from S. cerevisiae and C. elegans. Finally, we present an approach to model the relationship between metabolic pathways and operon structure in prokaryotic genomes. In this approach, we present a new pattern classâ biclusters over domains with supplied partial ordersâ and present algorithms for systematically detecting such biclusters. Together, our data mining algorithms provide a comprehensive arsenal of techniques for modeling gene perturbation screens and metabolic pathways.en_US
dc.publisherVirginia Techen_US
dc.relation.haspartJin_Ying_D_2009.pdfen_US
dc.rightsI hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Virginia Tech or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report.en_US
dc.subjectpartial ordersen_US
dc.subjectbiclustersen_US
dc.subjectgraph separatorsen_US
dc.subjectrelative importance methodsen_US
dc.subjectBiological networksen_US
dc.titleNew Algorithms for Mining Network Datasets: Applications to Phenotype and Pathway Modelingen_US
dc.typeDissertationen_US
dc.contributor.departmentComputer Scienceen_US
dc.description.degreePh. D.en_US
thesis.degree.namePh. D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineComputer Scienceen_US
dc.contributor.committeechairRamakrishnan, Narendranen_US
dc.contributor.committeememberFox, Edward Alanen_US
dc.contributor.committeememberHeath, Lenwood S.en_US
dc.contributor.committeememberMurali, T. M.en_US
dc.contributor.committeememberHelm, Richard Fredericken_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-12302009-142944/en_US
dc.date.sdate2009-12-30en_US
dc.date.rdate2010-01-22
dc.date.adate2010-01-22en_US


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