ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast algorithms for detecting overlapping functional modules in protein-protein interaction networks
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
An ACO based functional module detection algorithm for protein interaction networks
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Function-Function correlated multi-label protein function prediction over interaction networks
RECOMB'12 Proceedings of the 16th Annual international conference on Research in Computational Molecular Biology
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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 usefulness of this understanding is, however, typically compromised by noisy data. The effective way of integrating and using these non-congruent data sets has received little attention to date. This paper proposes a model to integrate different data sets. We construct this model using our prior knowledge of data set reliability. Based on this model, we propose a topological measurement to select reliable interactions and to quantify the similarity between two proteinsý interaction profiles. Our measurement exploits the small-world network topological properties of protein interaction network. Meanwhile, we discovered some additional properties of the network. We show that our measurement can be used to find reliable interactions with improved performance and to find protein pairs with higher function homogeneity.