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Theoretical Computer Science
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Communications of the ACM
Protein complex prediction via cost-based clustering
Bioinformatics
The worst-case time complexity for generating all maximal cliques and computational experiments
Theoretical Computer Science - Computing and combinatorics
Information Sciences: an International Journal
Maximum entropy membership functions for discrete fuzzy variables
Information Sciences: an International Journal
Integrating induction and deduction for noisy data mining
Information Sciences: an International Journal
Bootstrapping the interactome: unsupervised identification of protein complexes in yeast
RECOMB'08 Proceedings of the 12th annual international conference on Research in computational molecular biology
Information Sciences: an International Journal
Information Sciences: an International Journal
Accelerating spectral clustering with partial supervision
Data Mining and Knowledge Discovery
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Information Sciences: an International Journal
Greedy-type resistance of combinatorial problems
Discrete Optimization
Revealing network communities with a nonlinear programming method
Information Sciences: an International Journal
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Studying protein complexes is very important in biological processes because it helps reveal the structure-functionality relationships in a protein complex. Much attention has been paid to accurately predicting the protein complexes from the increasing amount of protein-protein interaction (PPI) data. Almost all of the current algorithms that concern the detection of protein complexes focus on discovering dense subgraphs based on the observation that dense subgraphs in a biological network may correspond to protein complexes. However, such an assumption would throw away further topological information about complexes. In this paper introducing the core-attachment concept, a novel core-attachment algorithm is developed by detecting the cores and attachments, respectively. To detect the cores of protein complexes, a virtual network is constructed using the eigenvalues and eigenvectors of the network involved, where each maximal clique corresponds to a protein complex core. With this notion, the problem of detecting protein complex cores is transformed into the classic all-cliques problem. The attachments are then merged into their cores to yield biologically meaningful complexes. A comprehensive comparison between MCL, DPClus, Coach, DECAFF and our algorithm has been made by comparing the predicted protein complexes with the benchmarked complexes. Experimental results indicate that our algorithm outperforms the MCL, DPClus and DECAFF and has comparative performance with the Coach algorithm in terms of the accuracy of prediction. Moreover, the detected complexes with core-attachment structures match well with the benchmark data, demonstrating that our algorithm can provide more insightful perspectives. Robustness analysis further shows that the algorithm is very robust.