title: A disease annotation study of gene signatures in a breast cancer microarray dataset.
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.
authors.affiliation: Department of Electronic and Computer Engineering, Technical University of Crete, Chania 73100, Greece.
authors.forename: Foivos
authors.initials: F
authors.lastname: Gypas
authors.forename: Ekaterini S
authors.initials: ES
authors.lastname: Bei
authors.forename: Michalis
authors.initials: M
authors.lastname: Zervakis
authors.forename: Stelios
authors.initials: S
authors.lastname: Sfakianakis
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000378:metabolism*; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000378:metabolism*; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D009363:Neoplasm Proteins / Q000378:metabolism*; D040081:Protein Array Analysis / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins