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BMEI
2008
IEEE

Clustering of High-Dimensional Gene Expression Data with Feature Filtering Methods and Diffusion Maps

8 years 4 months ago
Clustering of High-Dimensional Gene Expression Data with Feature Filtering Methods and Diffusion Maps
The importance of gene expression data in cancer diagnosis and treatment by now has been widely recognized by cancer researchers in recent years. However, one of the major challenges in the computational analysis of such data is the curse of dimensionality, due to the overwhelming number of measures of gene expression levels versus the small number of samples. Here, we use a two-step method to reduce the dimension of gene expression data. At first, we extract a subset of genes based on the statistical characteristics of their corresponding gene expression measurements. For further dimensionality reduction, we then apply diffusion maps, which interpret the eigenfunctions of Markov matrices as a system of coordinates on the original data set in order to obtain efficient representation of data geometric descriptions, to the reduced data. A neural network clustering theory, Fuzzy ART, is applied to the resulting data to generate clusters of cancer samples. Experimental results on the smal...
Rui Xu, Steven Damelin, Boaz Nadler, Donald C. Wun
Added 12 Oct 2010
Updated 12 Oct 2010
Type Conference
Year 2008
Where BMEI
Authors Rui Xu, Steven Damelin, Boaz Nadler, Donald C. Wunsch II
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