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Experimental techniques in biology such as DNA microarrays, serial analysis of gene expression and mass spectrometry proteomics, among others, are generating large amounts of data related to genes and proteins at different levels. One of the major challenges in the analysis of such datasets is to discover local structures composed by sets of genes or proteins that show coherent behavior patterns across subsets of experimental conditions. These patterns may provide clues about the main biological processes associated to different physiological states. In addition, as in any other experimental approach, it is necessary to analyze these data in the context of previously known information about the biological entities under study. Non-negative matrix factorization (NMF) technique has become very popular in this context due to the interpretability of the factors it generates. In this paper we will review two different applications of this methodology in this field and will provide some motivations for the application of similar techniques in the context of data analysis in biology.
Date of Conference: 18-21 May 2008