Comparative Analysis of Genomic Signal Processing for Microarray Data Clustering

What is it about?

Microarray data analysis provides better insight on understanding and linkage of genetic disorders in diseases such as diabetes, cardiovascular diseases and some forms of cancer. This process relies mainly on robust clustering, which aims at assigning observations defined in a high dimensional feature space, i.e. gene expression levels, into subsets sharing similar properties. This paper investigates Genomic signal processing techniques to perform that task.

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Why is it important?

This paper demonstrates that the introduction of the new paradigm of Genomic Signal Processing to microarray data clustering generates state of the art results. In addition to improved accuracy, this is particularly significant since those algorithms can be implemented on digital signal processors which would allow real-time processing.