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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/10725

Title: SLIDER: Mining correlated motifs in protein-protein interaction networks
Authors: BOYEN, Peter
NEVEN, Frank
van Dijk, Aalt D.J.
Van Ham, Roeland C.J.H.
Issue Date: 2009
Publisher: IEEE Computer Society
Citation: Proceedings of the 9th IEEE International Conference on Data Mining (ICDM 2009). p. 716-721.
Abstract: Correlated motif mining (CMM) is the problem to find overrepresented pairs of patterns, called motif pairs, in interacting protein sequences. Algorithmic solutions for CMM thereby provide a computational method for predicting binding sites for protein interaction. In this paper, we adopt a motif-driven approach where the support of candidate motif pairs is evaluated in the network. We experimentally establish the superiority of the Chi-square-based support measure over other support measures. Furthermore, we obtain that CMM is an NP-hard problem for a large class of support measures (including Chi-square) and reformulate the search for correlated motifs as a combinatorial optimization problem. We then present the method SLIDER which uses local search with a neigborhood function based on sliding motifs and employs the Chi-square-based support measure. We show that SLIDER outperforms existing motif-driven CMM methods and scales to large protein-protein interaction networks.
URI: http://hdl.handle.net/1942/10725
Link to publication: http://doi.ieeecomputersociety.org/10.1109/ICDM.2009.92
ISI #: 000287216600076
ISBN: 978-1-4244-5242-2
ISSN: 1550-4786
Category: C1
Type: Proceedings Paper
Validation: ecoom, 2012
Appears in Collections: Research publications

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