Computational analysis of microarray gene expression profiles: clustering, classification, and beyond

Abstract Gene array studies can assess the global expression patterns of thousands of genes under multiple conditions. This technology can provide important insights about the underlying genetic causes of many important biological questions, and can change our understanding of diseases, ultimately allowing the development of novel chemical entities as potential drug candidates. The informatics analysis and integration of gene expression pattern are critical for interpreting gene array studies. In this paper, we discuss the computational analysis of three important tasks: (1) the identification of differentially expressed genes, (2) the discovery of gene clusters, and (3) the classification of biological samples. In addition, we discuss how gene sequence and chemical structures can be profitably combined with microarray studies. Detailed examples are given throughout. Programs written in open source R language for achieving each of these tasks are freely available at gila.engr.uic.edu/genex.

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