A Study of HME Model in Time-Course Microarray Data

Myoung, Sung-Min;Kim, Dong-Geon;Jo, Jin-Nam

  • Received : 2012.01.25
  • Accepted : 2012.04.20
  • Published : 2012.06.30


For statistical microarray data analysis, clustering analysis is a useful exploratory technique and offers the promise of simultaneously studying the variation of many genes. However, most of the proposed clustering methods are not rigorously solved for a time-course microarray data cluster and for a fitting time covariate; therefore, a statistical method is needed to form a cluster and represent a linear trend of each cluster for each gene. In this research, we developed a modified hierarchical mixture of an experts model to suggest clustering data and characterize each cluster using a linear mixed effect model. The feasibility of the proposed method is illustrated by an application to the human fibroblast data suggested by Iyer et al. (1999).


Hierarchical Mixture of Experts;Mixture model;Linear Mixed Effect Model;Microarray


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Supported by : National Research Foundation of Korea(NRF)