Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Protein complex prediction via cost-based clustering
Bioinformatics
Iterative Cluster Analysis of Protein Interaction Data
Bioinformatics
A Two-Step Approach for Clustering Proteins Based on Protein Interaction Profile
BIBE '05 Proceedings of the Fifth IEEE Symposium on Bioinformatics and Bioengineering
A Graph-Theoretic Method for Mining Functional Modules in Large Sparse Protein Interaction Networks
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
PINCoC: a co-clustering based approach to analyze protein-protein interaction networks
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Improving functional modularity in protein-protein interactions graphs using hub-induced subgraphs
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Experimental evaluation of topological-based fitness functions to detect complexes in PPI networks
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Restricted neighborhood search clustering revisited: an evolutionary computation perspective
PRIB'13 Proceedings of the 8th IAPR international conference on Pattern Recognition in Bioinformatics
Analysing microarray expression data through effective clustering
Information Sciences: an International Journal
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Several approaches have been presented in the literature to cluster Protein-Protein Interaction (PPI) networks. They can be grouped in two main categories: those allowing a protein to participate in different clusters and those generating only nonoverlapping clusters. In both cases, a challenging task is to find a suitable compromise between the biological relevance of the results and a comprehensive coverage of the analyzed networks. Indeed, methods returning high accurate results are often able to cover only small parts of the input PPI network, especially when low-characterized networks are considered. We present a coclustering-based technique able to generate both overlapping and nonoverlapping clusters. The density of the clusters to search for can also be set by the user. We tested our method on the two networks of yeast and human, and compared it to other five well-known techniques on the same interaction data sets. The results showed that, for all the examples considered, our approach always reaches a good compromise between accuracy and network coverage. Furthermore, the behavior of our algorithm is not influenced by the structure of the input network, different from all the techniques considered in the comparison, which returned very good results on the yeast network, while on the human network their outcomes are rather poor.