유전자 발현 데이터의 독립 특징 부공간 해석

Independent Feature Subspace Analysis for Gene Expression Data

  • Kim, Heijin (Dept. of CSE, Pohang University of Science & Technology) ;
  • Park, Seungjin (Dept. of CSE, Pohang University of Science & Technology) ;
  • Bang, Sung-Yang (Dept. of CSE, Pohang University of Science & Technology)
  • 발행 : 2002.10.01

초록

This paper addresses a new statistical method, IFSAcycle, which is an unsupervised learning method of analyzing cell cycle-related gene expression data. The IFSAcycle is based on the independent feature subspace analysis (IFAS) [3], which generalizes the independent component analysis (ICA). Experimental results show the usefulness of IFAS: (1) the ability of assigning genes to multiple coexpression pattern groups; (2) the capability of clustering key genes that determine each critical point of cell cycle.

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