Clustering of high-dimensional gene expression data with feature filtering methods and diffusion maps

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ObjectiveThe importance of gene expression data in cancer diagnosis and treatment has become widely known by cancer researchers in recent years. However, one of the major challenges in the computational analysis of such data is the curse of dimensionality because of the overwhelming number of variables measured (genes) versus the small number of samples. Here, we use a two-step method to reduce the dimension of gene expression data and aim to address the problem of high dimensionality.

论文关键词:Clustering,Diffusion maps,Feature filtering,Fuzzy ART,Gene expression profiles

论文评审过程:Received 14 August 2008, Revised 24 June 2009, Accepted 30 June 2009, Available online 4 December 2009.

论文官网地址:https://doi.org/10.1016/j.artmed.2009.06.001