Identification of cancer patient subgroups via pathway based multi-view graph kernel clustering

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buir.advisorOkan, Öznur Taştan
dc.contributor.authorÜnal, Ali Burak
dc.date.accessioned2017-08-04T13:49:46Z
dc.date.available2017-08-04T13:49:46Z
dc.date.copyright2017-07
dc.date.issued2017-07
dc.date.submitted2017-08-04
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (M.S.): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2017.en_US
dc.descriptionIncludes bibliographical references (leaves 68-73).en_US
dc.description.abstractCharacterizing patient genomic alterations through next-generation sequencing technologies opens up new opportunities for re ning cancer subtypes. Di erent omics data provide di erent views into the molecular biology of the tumors. However, tumor cells exhibit high levels of heterogeneity, and di erent patients harbor di erent combinations of molecular alterations. On the other hand, different alterations may perturb the same biological pathways. In this work, we propose a novel clustering procedure that quanti es the similarities of patients from their alteration pro les on pathways via a novel graph kernel. For each pathway and patient pair, a vertex labeled undirected graph is constructed based on the patient molecular alterations and the pathway interactions. The proposed smoothed shortest path graph kernel (smSPK) assesses similarities of pair of patients with respect to a pathway by comparing their vertex labeled graphs. Our clustering procedure involves two steps. In the rst step, the smSPK kernel matrices for each pathway and data type are computed for patient pairs to construct multiple kernel matrices and in the ensuing step, these kernel matrices are input to a multi-view kernel clustering algorithm to stratify patients. We apply our methodology to 361 renal cell carcinoma patients, using somatic mutations, gene and protein expressions data. This approach yields subgroup of patients that di er signi cantly in their survival times (p-value 1:5 10􀀀8). The proposed methodology allows integrating other type of omics data and provides insight into disrupted pathways in each patient subgroup.en_US
dc.description.degreeM.S.en_US
dc.description.statementofresponsibilityby Ali Burak Ali.en_US
dc.embargo.release2019-07-31
dc.format.extentxiv, 88 pages : charts (some color) ; 29 cmen_US
dc.identifier.itemidB156081
dc.identifier.urihttp://hdl.handle.net/11693/33530
dc.language.isoEnglishen_US
dc.publisherBilkent Universityen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCancer subtypeen_US
dc.subjectGraph kernelen_US
dc.subjectMulti-view kernel clusteringen_US
dc.subjectKernel functionsen_US
dc.subjectClusteringen_US
dc.titleIdentification of cancer patient subgroups via pathway based multi-view graph kernel clusteringen_US
dc.title.alternativeKanser hasta alt gruplarının yolak esaslı çok bakışlı çizge çekirdeği gruplaması ile belirlenmesien_US
dc.typeThesisen_US

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