Mining breast cancer genetic data
Analyzing breast cancer gene expression data is a very challenging problem due to the large amount of genes examined. Computational techniques have proved reliable to make sense of large amounts of data like the data obtained from microarray analysis. In this study, we present a method to find a clu...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , |
| التنسيق: | conferenceObject |
| منشور في: |
2013
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| الوصول للمادة أونلاين: | http://hdl.handle.net/10725/7806 http://dx.doi.org/ 10.1109/ICNC.2013.6818131 http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://ieeexplore.ieee.org/abstract/document/6818131/ |
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| _version_ | 1864513467057176576 |
|---|---|
| author | Mansour, Nashat |
| author2 | Zantout, Rouba El-Sibai, Mirvat |
| author2_role | author author |
| author_facet | Mansour, Nashat Zantout, Rouba El-Sibai, Mirvat |
| author_role | author |
| dc.creator.none.fl_str_mv | Mansour, Nashat Zantout, Rouba El-Sibai, Mirvat |
| dc.date.none.fl_str_mv | 2013 2018-05-11T11:22:04Z 2018-05-11T11:22:04Z 2018-05-11 |
| dc.identifier.none.fl_str_mv | 9781467347143 http://hdl.handle.net/10725/7806 http://dx.doi.org/ 10.1109/ICNC.2013.6818131 Mansour, N., Zantout, R., & El-Sibai, M. (2013, July). Mining breast cancer genetic data. In 2013 Ninth International Conference on Natural Computation (ICNC) (pp. 1047-1051). IEEE. http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://ieeexplore.ieee.org/abstract/document/6818131/ |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | IEEE Lebanese American University |
| dc.rights.*.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.title.none.fl_str_mv | Mining breast cancer genetic data |
| dc.type.none.fl_str_mv | Conference Paper / Proceeding info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/conferenceObject |
| description | Analyzing breast cancer gene expression data is a very challenging problem due to the large amount of genes examined. Computational techniques have proved reliable to make sense of large amounts of data like the data obtained from microarray analysis. In this study, we present a method to find a clustering pattern of the genes involved in breast cancer. We design a growing hierarchical self-organizing map (GHSOM) to mine gene microarray data. We have applied GHSOM to 24,481 genes of DNA microarray of breast tumor samples. Our results have revealed 17 genes that are likely to be correlated with four breast cancer marker genes. |
| eu_rights_str_mv | openAccess |
| format | conferenceObject |
| id | LAURepo_2a984f94838f79cc3f24504461c6ea2d |
| identifier_str_mv | 9781467347143 Mansour, N., Zantout, R., & El-Sibai, M. (2013, July). Mining breast cancer genetic data. In 2013 Ninth International Conference on Natural Computation (ICNC) (pp. 1047-1051). IEEE. |
| language_invalid_str_mv | en |
| network_acronym_str | LAURepo |
| network_name_str | Lebanese American University repository |
| oai_identifier_str | oai:laur.lau.edu.lb:10725/7806 |
| publishDate | 2013 |
| publisher.none.fl_str_mv | IEEE Lebanese American University |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Mining breast cancer genetic dataMansour, NashatZantout, RoubaEl-Sibai, MirvatAnalyzing breast cancer gene expression data is a very challenging problem due to the large amount of genes examined. Computational techniques have proved reliable to make sense of large amounts of data like the data obtained from microarray analysis. In this study, we present a method to find a clustering pattern of the genes involved in breast cancer. We design a growing hierarchical self-organizing map (GHSOM) to mine gene microarray data. We have applied GHSOM to 24,481 genes of DNA microarray of breast tumor samples. Our results have revealed 17 genes that are likely to be correlated with four breast cancer marker genes.N/AIEEELebanese American University2018-05-11T11:22:04Z2018-05-11T11:22:04Z20132018-05-11Conference Paper / Proceedinginfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject9781467347143http://hdl.handle.net/10725/7806http://dx.doi.org/ 10.1109/ICNC.2013.6818131Mansour, N., Zantout, R., & El-Sibai, M. (2013, July). Mining breast cancer genetic data. In 2013 Ninth International Conference on Natural Computation (ICNC) (pp. 1047-1051). IEEE.http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.phphttps://ieeexplore.ieee.org/abstract/document/6818131/eninfo:eu-repo/semantics/openAccessoai:laur.lau.edu.lb:10725/78062021-03-19T10:03:30Z |
| spellingShingle | Mining breast cancer genetic data Mansour, Nashat |
| status_str | publishedVersion |
| title | Mining breast cancer genetic data |
| title_full | Mining breast cancer genetic data |
| title_fullStr | Mining breast cancer genetic data |
| title_full_unstemmed | Mining breast cancer genetic data |
| title_short | Mining breast cancer genetic data |
| title_sort | Mining breast cancer genetic data |
| url | http://hdl.handle.net/10725/7806 http://dx.doi.org/ 10.1109/ICNC.2013.6818131 http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://ieeexplore.ieee.org/abstract/document/6818131/ |