Summary
With the growing amount of genetic data available to scientists there is a pressing need to characterise the functions of genes. Such knowledge will enable us to better understand organisms at the molecular level and to elucidate the mechanisms by which diseases disrupt biological processes. With the advent of whole genome expression technologies such as DNA microarrays and proteomics, scientists can at last determine how the genes and proteins change their rates of expression under specific experimental conditions. The data sets generated from such studies are large and require sophisticated tools for proper analysis. In this chapter we review several techniques employed in clustering data sets of this type. Clustering can often reveal broad patterns which show that certain genes or proteins are performing common functions. This is a useful way in which one can attribute functions to newly discovered genes. A wide variety of clustering algorithms exists; we consider several of the most promising and look at how the techniques perform when tested with different types of data from gene expression and protein expression experiments.
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Patel, K., Cartwright, H.M. (2003). Clustering of Large Data Sets in the Life Sciences. In: Cartwright, H.M., Sztandera, L.M. (eds) Soft Computing Approaches in Chemistry. Studies in Fuzziness and Soft Computing, vol 120. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36213-5_2
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DOI: https://doi.org/10.1007/978-3-540-36213-5_2
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