Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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Identification of prognosis-specific network and prediction for estrogen receptor-negative breast cancer using microarray data and PPI data

마이크로어레이 데이터와 PPI 데이터를 이용한 에스트로겐 수용체 음성 유방암 환자의 예후 특이 네트워크 식별 및 예후 예측

  • Hwang, Youhyeon (Dept. of Computer Engineering, Gachon University) ;
  • Oh, Min (Dept. of Computer Engineering, Gachon University) ;
  • Yoon, Youngmi (Dept. of Computer Engineering, Gachon University)
  • 황유현 (가천대학교 컴퓨터공학과) ;
  • 오민 (가천대학교 컴퓨터공학과) ;
  • 윤영미 (가천대학교 컴퓨터공학과)
  • Received : 2014.11.12
  • Accepted : 2015.01.29
  • Published : 2015.02.28
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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 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.

본 논문에서는 유전자 네트워크를 기반으로 유방암 환자의 예후를 예측하는 알고리듬을 제안한다. 유방암 환자의 마이크로어레이 데이터와 PPI(Protein-protein interaction)데이터를 이용하여 알고리듬의 분류자로 사용될 예후 특이 네트워크(Prognosis specific gene network)를 추출한다. PPI에 속한 모든 유전자 네트워크에 대하여 각각의 네트워크가 예후 좋음과 나쁨을 잘 구분하는지에 대한 점수를 피어슨 상관계수(Pearson's correlation coefficient)와 마이크로어레이 데이터를 이용하여 계산한다. 이들 중 가장 예후에 유의한 네트워크를 식별하고, 이 네트워크를 분류자로 사용하여 에스트로겐 수용체 음성 유방암 환자의 예후를 분류 분석 한다. 본 연구와 기존 연구의 알고리듬 정확도를 비교 분석 하기 위하여 독립 실험을 진행하고, 본 연구에서 제안된 알고리듬의 성능이 더 우수함을 보인다. 또한, Gene Ontology 데이터베이스를 활용하여 식별된 예후 특이 네트워크를 기능적으로 검증 한다.

Keywords

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