Show simple item record

dc.contributor.advisorAjwad, Rasif
dc.contributor.authorFatema, Kaniz
dc.contributor.authorShabnam, Shejuti
dc.contributor.authorSaha, Akash
dc.date.accessioned2019-06-27T07:16:04Z
dc.date.available2019-06-27T07:16:04Z
dc.date.copyright2019
dc.date.issued2019-04
dc.identifier.otherID 19141026
dc.identifier.otherID 19141029
dc.identifier.otherID 15101085
dc.identifier.urihttp://hdl.handle.net/10361/12266
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2019.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 46-52).
dc.description.abstractThe purpose of cancer genome project is to classify the genetic variations that are related to clinical phenotypes. However, some studies showed that some specific cellular pathways are targeted by the cancer mutations genes. But a few of the pathway genes are mutated in each patient. In most approaches, only the existing pathways are considered and the topology of the pathways are ignored. Consequently, new attempts have been targeted on classifying significantly mutated subnetworks and combining them with cancer survival. We had proposed a novel bioinformatics pipeline to identify quantitative classification of the breast cancer genome to verify if the steps will be working or not on real dataset. We have generated a mutation matrix from the collected dataset and calculated pairwise gene similarity. After that, we have also done clustering of the identified cancer gene network, which may help cancer patients by suggesting optimal treatments. We hope our pipeline can also be used for other types of mutation data analysis.en_US
dc.description.statementofresponsibilityKaniz Fatema
dc.description.statementofresponsibilityShejuti Shabnam
dc.description.statementofresponsibilityAkash Saha
dc.format.extent52 pages
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectCanceren_US
dc.subjectGene sub networksen_US
dc.subjectGene similarityen_US
dc.subjectBioinformaticsen_US
dc.subjectPathwaysen_US
dc.subjectClusteringen_US
dc.subject.lcshCluster analysis.
dc.titleQualitative classification of the breast cancer genome and clustering of the cancer gene networken_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, Brac University
dc.description.degreeB. Computer Science and Engineering


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record