Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Association Analysis Techniques for Bioinformatics Problems
BICoB '09 Proceedings of the 1st International Conference on Bioinformatics and Computational Biology
Predicting protein-protein interactions using numerical associational features
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
Identification of DNA-Binding and Protein-Binding Proteins Using Enhanced Graph Wavelet Features
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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The recent high-throughput bio-techniques have provided us large-scale protein-protein interaction data through systematic identification of physical and genetic interactions among all proteins in an organism. Several previous studies have shown that using protein-protein interaction networks to predict protein function is a big step toward full understanding of the mechanisms of cells. However, the protein-protein interaction data derived from high-throughput experiments are typically very noisy, which presents great challenges to the existing methods. In this paper, we propose a novel common-neighborbased model and a Bayesian framework to predict protein function on the basis of the small-world property of the protein-protein interaction network. We tested our approach on five data sets from various sources. The experimental results have shown that our approach has a better performance than several representative methods in terms of both precision and recall. In addition, our method is particularly effective to handle the high false-positive and false-negative rates in protein-protein interaction data.