KIPS Transactions on Software and Data Engineering (정보처리학회논문지:소프트웨어 및 데이터공학)

Korea Information Processing Society (한국정보처리학회)
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Cancer Patient Specific Driver Gene Identification by Personalized Gene Network and PageRank

개인별 유전자 네트워크 구축 및 페이지랭크를 이용한 환자 특이적 암 유발 유전자 탐색 방법

  • Received : 2021.09.16
  • Accepted : 2021.10.28
  • Published : 2021.12.31
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Abstract

Cancer patients can have different kinds of cancer driver genes, and identification of these patient-specific cancer driver genes is an important step in the development of personalized cancer treatment and drug development. Several bioinformatic methods have been proposed for this purpose, but there is room for improvement in terms of accuracy. In this paper, we propose NPD (Network based Patient-specific Driver gene identification) for identifying patient-specific cancer driver genes. NPD consists of three steps, constructing a patient-specific gene network, applying the modified PageRank algorithm to assign scores to genes, and identifying cancer driver genes through a score comparison method. We applied NPD on six cancer types of TCGA data, and found that NPD showed generally higher F1 score compared to existing patient-specific cancer driver gene identification methods.

암을 유발하는 유전자는 모든 암 환자에게 공통적인 것은 아니며, 이러한 환자 특이적 암 유발 유전자의 탐색은 개인 맟춤형 암 치료 및 항암제 개발에 있어서 매우 중요하다. 환자 특이적 암 유발 유전자를 찾기 위한 생물 정보학 연구들이 있어왔지만, 아직 정확도 면에서는 발전의 여지가 있다. 본 논문에서는 환자 특이적 암 유발 유전자를 탐색하기 위하여 NPD (Network based Patient-specific Driver gene identification)라는 방법을 제안한다. NPD는 환자 특이적 유전자 네트워크를 구축하고, 여기에 수정된 PageRank 알고리즘을 적용하여 유전자에 점수를 부여한 후, 유전적 변이 데이터를 사용한 승률 계산 방법을 통하여 암 유발 유전자를 찾는 세 단계로 이루어진다. TCGA 데이터 베이스의 여섯 개의 암 데이터에 NPD를 적용한 결과, NPD가 기존의 환자 특이적 암 유발 유전자 탐색 방법들보다 전체적으로 높은 F1 점수를 보여줌을 확인할 수 있었다.

Keywords

Acknowledgement

이 논문은 인천대학교 자체연구비(2021-0097) 의하여 연구되었음.

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