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 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 | Document URI: | http://hdl.handle.net/1942/10725 | Link to publication/dataset: | http://doi.ieeecomputersociety.org/10.1109/ICDM.2009.92 http://hdl.handle.net/1942/9865 |
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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File | Description | Size | Format | |
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icdm09-cameraready.pdf | Peer-reviewed author version | 211.16 kB | Adobe PDF | View/Open |
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