SLIDER: Mining Correlated Motifs in Protein-Protein Interaction Networks

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 χ2 -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 χ2 ) 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 χ2 -based support measure. We show that SLIDER outperforms existing motif-driven CMM methods and scales to large protein-protein interaction networks.

• Type: Conference
• Year: 2009
• Where: ICDM
• Authors: Peter Boyen, Frank Neven, Dries Van Dyck, Aalt-Jan van Dijk, Roeland C. H. J. van Ham
• Keywords: -based Support Measure, Data Mining, ICDM 2009, Motif Pairs, Support Measure