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Extra info for ACM transactions on information systems (January)

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Lymphoma [Alizadeh et al. 2000], and the clustering of both the tissues and the genes on microarrays for normal and cancerous colon tissue [Alon et al. 1999]. , Tavazoie et al. , Golub et al. , Yeung et al. [2001]), and specially designed algorithms such as the method of Ben-Dor et al. [1999] which is based on a biological model of genes. Reports on cluster analysis of SAGE data have only recently started to appear (including some preliminary results, which we extend in this article [Ng et al. 2001].

A substantive resource of SAGE data has been created as part of the SageMap initiative of the National Cancer Institute, USA [Lash et al. 2000], now part of the Gene Expression Omnibus [Edgar et al. 2002]. The availability of such a resource allows for a global comparison of normal tissues, cell lines and cancer types. Such a comparison may shed light on many interesting questions, such as: r Is the SAGE method robust? Different libraries were created at different places by different groups. Is the SAGE method itself reproducible?

Many single-stranded pieces of DNA that ACM Transactions on Information Systems, Vol. 23, No. 1, January 2005. A Methodology for Analyzing SAGE Libraries for Cancer Profiling • 37 complement these short sequences are printed on a glass chip. The chip is then brought into contact with the mRNAs extracted from a cell sample. The mRNA in the sample binds to its DNA complement on the chip, and causes it to fluoresce. This fluorescence is detected with a laser. The more mRNA of a certain sequence is in the sample, the more its complementary spot on the chip will light up.

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