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 neighborhood 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.
|Publication status||Published - 2009|
|Event||IEE International Conference on Data Mining (ICDM 2009), Miami, Florida, USA - |
Duration: 6 Dec 2009 → 9 Dec 2009
|Conference||IEE International Conference on Data Mining (ICDM 2009), Miami, Florida, USA|
|Period||6/12/09 → 9/12/09|