Abstract
Molecular mechanisms of plant-pathogen interaction have been studied thoroughly because of its importance for crop production and food supply. This knowledge is a starting point in order to identify new and specific resistance genes by detecting similar expression patterns. Here we evaluate the usefulness of clustering and data-mining methods to group together known plant resistance genes based on expression profiles. We conduct clustering separately on P.infestans inoculated and not-inoculated tomatoes and conclude that conducting the analysis separately is important for each condition, because grouping is different reflecting a characteristic behavior of resistance genes in presence of the pathogen.
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López-Kleine, L., Romeo, J., Torres-Avilés, F. (2013). Gene Functional Prediction Using Clustering Methods for the Analysis of Tomato Microarray Data. In: Mohamad, M., Nanni, L., Rocha, M., Fdez-Riverola, F. (eds) 7th International Conference on Practical Applications of Computational Biology & Bioinformatics. Advances in Intelligent Systems and Computing, vol 222. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00578-2_1
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DOI: https://doi.org/10.1007/978-3-319-00578-2_1
Publisher Name: Springer, Heidelberg
Print ISBN: 978-3-319-00577-5
Online ISBN: 978-3-319-00578-2
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