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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| Other Authors: | , |
| Format: | conferenceObject |
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2013
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| Online Access: | 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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| Summary: | 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. |
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