Show simple item record

dc.contributor.supervisor Hu, Pingzhao (Biochemistry and Medical Genetics) Domaratzki, Michael (Computer Science) en_US
dc.contributor.author Ajwad, Rasif
dc.date.accessioned 2017-09-19T18:04:26Z
dc.date.available 2017-09-19T18:04:26Z
dc.date.issued 2017
dc.identifier.uri http://hdl.handle.net/1993/32634
dc.description.abstract Cancer 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.subject Copy number variation, Breast Cancer, Gene interaction network, Subnetwork, Survival analysis en_US
dc.title Identification of significantly mutated subnetworks in the breast cancer genome en_US
dc.degree.discipline Computer Science en_US
dc.contributor.examiningcommittee Tremblay-Savard, Olivier (Computer Science) Chen, Guanqun (Biological Sciences) en_US
dc.degree.level Master of Science (M.Sc.) en_US
dc.description.note February 2018 en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

View Statistics