Precision and recall of machine translation
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
Protein complex prediction via cost-based clustering
Bioinformatics
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
ACOPIN: An ACO Algorithm with TSP Approach for Clustering Proteins from Protein Interaction Network
EMS '08 Proceedings of the 2008 Second UKSIM European Symposium on Computer Modeling and Simulation
Protein Interaction Networks: Computational Analysis
Protein Interaction Networks: Computational Analysis
Clustering protein interaction data through chaotic genetic algorithm
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Information Sciences: an International Journal
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Clustering Protein-Protein Interaction (PPI) data is a difficult problem due to its small world and scale-free characteristics. Existing clustering methods could not perform well. This paper proposes an improved functional-flow based approach through Quantum-behaved Particle Swarm Optimisation (QPSO) algorithm, which can find the optimum threshold automatically when calculating the lowest similarity between modules. We also take bridging nodes into account to improve the clustering result. The experiments on Munich Information Center for Protein Sequences (MIPS) PPI data sets show that the algorithm has better performance than functional flow method in terms of accuracy and number of matched clusters.