A disease annotation study of gene signatures in a breast cancer microarray dataset

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 a...

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Bibliographic Details
Main Authors: Gypas, F., Bei, E. S., Zervakis, M., Sfakianakis, S.
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Breast cancer
Diseases
Logic gates
Proteins
Support vector machines
Online Access:Request full text
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Summary: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.
ISSN:1094-687X
1558-4615
2694-0604
DOI:10.1109/IEMBS.2011.6091416