Title :
A disease annotation study of gene signatures in a breast cancer microarray dataset
Author :
Gypas, Foivos ; Bei, Ekaterini S. ; Zervakis, Michalis ; Sfakianakis, Stelios
Author_Institution :
Dept. of Electron. & Comput. Eng., Tech. Univ. of Crete, Chania, Greece
fDate :
Aug. 30 2011-Sept. 3 2011
Abstract :
Breast cancer is a complex disease with heterogeneity between patients regarding prognosis and treatment response. Recent progress in advanced molecular biology techniques and the development of efficient methods for database mining lead to the discovery of promising novel biomarkers for prognosis and prediction of breast cancer. In this paper, we applied three computational algorithms (RFE-LNW, Lasso and FSMLP) to one microarray dataset for breast cancer and compared the obtained gene signatures with a recently described disease-agnostic tool, the Genotator. We identified a panel of 152 genes as a potential prognostic signature and the ERRFI1 gene as possible biomarker of breast cancer disease.
Keywords :
biological organs; cancer; genetics; gynaecology; lab-on-a-chip; medical computing; multilayer perceptrons; ERRFI1 gene; FSMLP computational algorithm; Lasso computational algorithm; RFE-LNW computational algorithm; biomarker; breast cancer disease; breast cancer microarray dataset; computational algorithms; disease annotation; gene signature; prognostic signature; Accuracy; Breast cancer; Diseases; Logic gates; Proteins; Support vector machines; Algorithms; Breast Neoplasms; Female; Gene Expression Profiling; Humans; Neoplasm Proteins; Protein Array Analysis; Reproducibility of Results; Sensitivity and Specificity; Tumor Markers, Biological;
Conference_Titel :
Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE
Conference_Location :
Boston, MA
Print_ISBN :
978-1-4244-4121-1
Electronic_ISBN :
1557-170X
DOI :
10.1109/IEMBS.2011.6091416
