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

Foivos Gypas; Ekaterini S Bei; Michalis Zervakis; Stelios Sfakianakis

doi:10.1109/IEMBS.2011.6091416

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

Annu Int Conf IEEE Eng Med Biol Soc. 2011:2011:5551-4. doi: 10.1109/IEMBS.2011.6091416.

Authors

Foivos Gypas¹, Ekaterini S Bei, Michalis Zervakis, Stelios Sfakianakis

Affiliation

¹ Department of Electronic and Computer Engineering, Technical University of Crete, Chania 73100, Greece.

PMID: 22255596
DOI: 10.1109/IEMBS.2011.6091416

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms*
Biomarkers, Tumor / metabolism*
Breast Neoplasms / diagnosis*
Breast Neoplasms / metabolism*
Female
Gene Expression Profiling / methods*
Humans
Neoplasm Proteins / metabolism*
Protein Array Analysis / methods*
Reproducibility of Results
Sensitivity and Specificity

Substances

Biomarkers, Tumor
Neoplasm Proteins