Qualitative classification of the breast cancer genome and clustering of the cancer gene network
| bracu.degree.level | Undergraduate | |
| bracu.type.group | Student Works | |
| datacite.rights | Open Access | |
| dc.contributor.advisor | Ajwad, Rasif | |
| dc.contributor.author | Fatema, Kaniz | |
| dc.contributor.author | Shabnam, Shejuti | |
| dc.contributor.author | Saha, Akash | |
| dc.contributor.department | Department of Computer Science and Engineering | |
| 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.description | Cataloged from PDF version of thesis. | |
| dc.description | Includes bibliographical references (pages 46-52). | |
| 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.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.degree | Bachelor of Science in Computer Science and Engineering | |
| dc.description.statementofresponsibility | Kaniz Fatema | |
| dc.description.statementofresponsibility | Shejuti Shabnam | |
| dc.description.statementofresponsibility | Akash Saha | |
| dc.format.extent | 52 pages | |
| 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.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 |