SLIDER: Mining Correlated Motifs in Protein-Protein Interaction Networks
Miami, FL(2009)
摘要
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.
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关键词
combinatorial optimization problem,motif-driven cmm method,mining correlated motifs,chi-square-based support measure,correlated motif,correlated motif mining,computational method,motif pair,candidate motif pair,support measure,protein-protein interaction networks,np-hard problem,graph theory,protein sequence,proteins,binding site,local search,np hard problem,force,data mining,genetics,noise
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