Clustering expression data

Computational methods have been applied to determine the connections in systems that are not well-defined by canonical pathways. This is either done by semi-automated and/or curated literature causal modeling [1] or by statistical methods based on large-scale data from expression or proteomic studies (a mostly theoretical approach is given by reference [2] and a more applied approach is in reference [3]). Many methods, including clustering, Bayesian analysis and principal component analysis have been used to find relationships and "fingerprints" in gene expression data [4]. 

Qin LX, Self SG (2006) The clustering of regression models method with applications in gene expression data. Biometrics 62 526-533. doi BIOM498 (pii)lO.llll/ j.l541-0420.2005.00498.x... 

All software produced by Eisen lab may be downloaded and used free of charge by academic and other non-profit researchers. Software is available for microarray image analysis, cluster analysis and visualization and combined expression data and sequence analysis. 

Iazzetti G, Calabro V, Saviozzi S, Weisz A, Lania L, Calogero R, BSC A clustering program for DNA array expression data, Proceedings of Biocomp, 2001, Siena, 2001. http //obelix.bio.uniroma2.it/www/abstr 2001. html iazzetti... 

Belacel N, Wang Q, Cuperlovic-Culf M. Clustering methods for 102. microarray gene expression data. OMICS. 2006 10 507-531. 

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