A Coclustering Approach for Mining Large Protein-Protein Interaction Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
EvoBIO'12 Proceedings of the 10th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
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
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High-throughput methods for detecting protein-protein interactions (PPI) have given researchers an initial global picture of protein interactions on a genomic scale. The huge data sets generated by such experiments pose new challenges in data analysis. Though clustering methods have been successfully applied in many areas in bioinformatics, many clustering algorithms cannot be readily applied on protein interaction data sets. One main problem is that the similarity between two proteins cannot be easily defined. This paper proposes a probabilistic model to define the similarity based on conditional probabilities. We then propose a two-step method for estimating the similarity between two proteins based on protein interaction profile. In the first step, the model is trained with proteins with known annotation. Based on this model, similarities are calculated in the second step. Experiments show that our method improves performance.