dc.contributor.advisor | Ajwad, Rasif | |
dc.contributor.author | Fatema, Kaniz | |
dc.contributor.author | Shabnam, Shejuti | |
dc.contributor.author | Saha, Akash | |
dc.date.accessioned | 2019-06-27T07:16:04Z | |
dc.date.available | 2019-06-27T07:16:04Z | |
dc.date.copyright | 2019 | |
dc.date.issued | 2019-04 | |
dc.identifier.other | ID 19141026 | |
dc.identifier.other | ID 19141029 | |
dc.identifier.other | ID 15101085 | |
dc.identifier.uri | http://hdl.handle.net/10361/12266 | |
dc.description | This 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.description | Cataloged from PDF version of thesis. | |
dc.description | Includes bibliographical references (pages 46-52). | |
dc.description.abstract | The 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.statementofresponsibility | Kaniz Fatema | |
dc.description.statementofresponsibility | Shejuti Shabnam | |
dc.description.statementofresponsibility | Akash Saha | |
dc.format.extent | 52 pages | |
dc.language.iso | en | en_US |
dc.publisher | BRAC University | en_US |
dc.rights | Brac 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.subject | Cancer | en_US |
dc.subject | Gene sub networks | en_US |
dc.subject | Gene similarity | en_US |
dc.subject | Bioinformatics | en_US |
dc.subject | Pathways | en_US |
dc.subject | Clustering | en_US |
dc.subject.lcsh | Cluster analysis. | |
dc.title | Qualitative classification of the breast cancer genome and clustering of the cancer gene network | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Department of Computer Science and Engineering, Brac University | |
dc.description.degree | B. Computer Science and Engineering | |