Title :
Mining breast cancer genetic data
Author :
Mansour, Nehad ; Zantout, Rached ; El-Sibai, Mirvat
Author_Institution :
Dept. of Comput. Sci. & Math., Lebanese American Univ., Beirut, Lebanon
Abstract :
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.
Keywords :
biology computing; cancer; data mining; pattern clustering; self-organising feature maps; DNA microarray; GHSOM; breast cancer gene expression data; breast cancer genetic data mining; breast tumor samples; computational techniques; hierarchical self-organizing map; microarray analysis; pattern clustering; Breast cancer; Breast tumors; Diseases; Gene expression; Proteins; Vectors; Bioinformatics; Breast cancer; Clustering; Growing hierarchical self-organizing map; Microarray data analysis; Self-organizing map;
Conference_Titel :
Natural Computation (ICNC), 2013 Ninth International Conference on
Conference_Location :
Shenyang
DOI :
10.1109/ICNC.2013.6818131
