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Title: SLIDER: Mining correlated motifs in protein-protein interaction networks
Authors: BOYEN, Peter 
NEVEN, Frank 
VAN DYCK, Dries 
van Dijk, Aalt D.J.
Van Ham, Roeland C.J.H.
Issue Date: 2009
Publisher: IEEE Computer Society
Source: 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.
Keywords: correlated motifs; PPI networks; local search
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ISBN: 978-1-4244-5242-2
ISI #: 000287216600076
Category: C1
Type: Proceedings Paper
Validations: ecoom 2012
Appears in Collections:Research publications

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