The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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A hybrid method to compose an optimal gene set for multi-class classification using mRMR and modified particle swarm optimization

mRMR과 수정된 입자군집화 방법을 이용한 다범주 분류를 위한 최적유전자집단 구성

  • Lee, Sunho (Division of Mathematics and Statistics, Sejong University)
  • 이선호 (세종대학교 수학통계학부)
  • Received : 2020.08.24
  • Accepted : 2020.10.19
  • Published : 2020.12.31
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Abstract

The aim of this research is to find an optimal gene set that provides highly accurate multi-class classification with a minimum number of genes. A two-stage procedure is proposed: Based on minimum redundancy and maximum relevance (mRMR) framework, several statistics to rank differential expression genes and K-means clustering to reduce redundancy between genes are used for data filtering procedure. And a particle swarm optimization is modified to select a small subset of informative genes. Two well known multi-class microarray data sets, ALL and SRBCT, are analyzed to indicate the effectiveness of this hybrid method.

표본의 다범주 표현형을 예측하는데 사용되는 최적의 유전자집단이란 적은 수의 유전자로 표현형을 정확히 예측할 수 있는 유전자들의 모임이다. 특이발현유전자를 검색하는 통계량은 이미 여러 가지가 있고, K-평균 군집화를 곁들여 중복성이 적은 특이발현유전자들을 선택 가능하다. 이들을 바탕으로 적은 수로 정확하게 다범주 분류가 가능한 유전자집단을 구성할 수 있도록 수정한 입자최적화 방법을 제안한다. 널리 알려진 ALL 248례와 SRBCT 83례를 이용하여 제안된 방법으로 최적유전자집단을 찾을 수 있음을 보였다.

Keywords

References

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