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This study proposes an algorithm for predicting breast cancer prognosis based on genetic network. We identify prognosis-specific network using gene expression data and PPI(protein-protein interaction) data. To acquire the network, we calculate Pearson's correlation coefficient(PCC) between genes in all PPI pairs ∙1 :  ∙2 :  ∙ :  ∙ : 2014. 11. 12,  : 2014. 12. 31,  : 2015. 1. 29. *  (Dept. of Computer Engineering, Gachon University) **  ()(Dept. of Computer Engineering, Gachon University) ※  ()       (NRF-2010-0008639). 138 Journal of The Korea Society of Computer and Information February 2015 using gene expression data. We develop a prediction model for breast cancer patients with estrogen-receptor-negative using the network as a classifier. We compare classification performance of our algorithm with existing algorithms on independent data and shows our algorithm is improved. In addition, we make an functionality analysis on the genes in the prognosis-specific network using GO(Gene Ontology) enrichment validation. ▸

Identification of prognosis-specific network and prediction for estrogen receptor-negative breast cancer using microarray data and PPI data