Abstract
Within a gene expression matrix, there are usually several particular macroscopic phenotypes of samples related to some diseases or drug effects, such as diseased samples, normal samples or drug treated samples. The goal of sample-based clustering is to find the phenotype structures of these samples. A novel method for automatically discovering clusters of samples which are coherent from a genetic point of view is evaluated on publicly available datasets. Each possible cluster is characterized by a fuzzy pattern which maintains a fuzzy discretization of relevant gene expression values. Possible clusters are randomly constructed and iteratively refined by following a probabilistic search and an optimization schema.
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Gómez, H., Glez-Peña, D., Reboiro-Jato, M., Pavón, R., Díaz, F., Fdez-Riverola, F. (2010). An Experimental Evaluation of a Novel Stochastic Method for Iterative Class Discovery on Real Microarray Datasets. In: Rocha, M.P., Riverola, F.F., Shatkay, H., Corchado, J.M. (eds) Advances in Bioinformatics. Advances in Intelligent and Soft Computing, vol 74. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13214-8_2
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DOI: https://doi.org/10.1007/978-3-642-13214-8_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13213-1
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