Abstract
DNA microarrays can provide information about the expression levels of thousands of genes, however these measurements are affected by errors and noise; moreover biological processes develop in very different time scales. A way to cope with these uncertain data is to represent expression level signals in a symbolic way and to adapt sub-string matching algorithms (such as the Longest Common Subsequence) for reconstructing the underlying regulatory network. In this work a first simple task of deciding the regulation direction given a set of correlated genes is studied. As a validation test, the approach is applied to four biological datasets composed of Yeast cell-cycle regulated genes under different synchronization methods.
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Badaloni, S., Falda, M. (2010). Coping with Uncertainty in Temporal Gene Expressions Using Symbolic Representations. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications. IPMU 2010. Communications in Computer and Information Science, vol 81. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14058-7_2
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DOI: https://doi.org/10.1007/978-3-642-14058-7_2
Publisher Name: Springer, Berlin, Heidelberg
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