Abstract
Clustering is a technique extensively employed for the analysis, classification and annotation of DNA microarrays. In particular clustering based upon the classical combinatorial curvature is widely applied. We introduce a new clustering method for vertex-weighted networks, method which is based upon a generalization of the combinatorial curvature. The new measure is of a geometric nature and represents the metric curvature of the network, perceived as a finite metric space. The metric in question is natural one, being induced by the weights. We apply our method to publicly available yeast and human lymphoma data. We believe this method provides a much more delicate, graduate method of clustering then the other methods which do not undertake to ascertain all the relevant data. We compare our results with other works. Our implementation is based upon Trixy (as available at http://tagc.univ-mrs.fr/bioinformatics/trixy.html ), with some appropriate modifications to befit the new method.
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Saucan, E., Appleboim, E. (2005). Curvature Based Clustering for DNA Microarray Data Analysis. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds) Pattern Recognition and Image Analysis. IbPRIA 2005. Lecture Notes in Computer Science, vol 3523. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11492542_50
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DOI: https://doi.org/10.1007/11492542_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26154-4
Online ISBN: 978-3-540-32238-2
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