Abstract
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 ∙제1저자 : 황유현 ∙제2저자 : 오민 ∙교신저자 : 윤영미 ∙투고일 : 2014. 11. 12, 심사일 : 2014. 12. 31, 게재확정일 : 2015. 1. 29. * 가천대학교 컴퓨터공학과(Dept. of Computer Engineering, Gachon University) **가천대학교 컴퓨터공학과 교수(교신저자)(Dept. of Computer Engineering, Gachon University) ※이 논문은 정부(교육과학기술부)의 재원으로 한국연구재단의 기초연구사업 지원을 받아 수행된 것임(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. ▸
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