Identification of significantly mutated subnetworks in the breast cancer genome

dc.contributor.authorAjwad, Rasif
dc.contributor.examiningcommitteeTremblay-Savard, Olivier (Computer Science) Chen, Guanqun (Biological Sciences)en_US
dc.contributor.supervisorHu, Pingzhao (Biochemistry and Medical Genetics) Domaratzki, Michael (Computer Science)en_US
dc.date.accessioned2017-09-19T18:04:26Z
dc.date.available2017-09-19T18:04:26Z
dc.date.issued2017
dc.degree.disciplineComputer Scienceen_US
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.abstractCancer genome projects aim at identifying the genetic variations that are related to clinical phenotypes. Recent studies showed that cancer mutations target genes that are in specific cellular pathways. New efforts have been focused on identifying significantly mutated subnetworks and associating them with cancer survival. We developed a novel bioinformatics analysis pipeline to identify significantly mutated subnetworks in the breast cancer genome. Our goals are to evaluate whether the identified subnetworks can be used as biomarkers for predicting breast cancer patient survival and provide the mechanisms of the pathways enriched in the subnetworks. We identified a significantly mutated yet functionally relevant subnetwork using two graph-based clustering algorithms. The genes in the subnetwork are significantly enriched in the retinol metabolism KEGG pathway. Our study showed that the new bioinformatics pipeline has the potential to identify new network-based biomarkers, which may be useful for stratifying cancer patients for choosing optimal treatments.en_US
dc.description.noteFebruary 2018en_US
dc.identifier.urihttp://hdl.handle.net/1993/32634
dc.language.isoengen_US
dc.rightsopen accessen_US
dc.subjectCopy number variation, Breast Cancer, Gene interaction network, Subnetwork, Survival analysisen_US
dc.titleIdentification of significantly mutated subnetworks in the breast cancer genomeen_US
dc.typemaster thesisen_US
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