Interactome data and databases: different types of protein interaction: Conference Reviews
Comparative and Functional Genomics
Comparison of human protein--protein interaction maps
Bioinformatics
Assessing the functional structure of genomic data
Bioinformatics
Protein-Protein Interaction Prediction and Assessment from Model Organisms
BIBM '08 Proceedings of the 2008 IEEE International Conference on Bioinformatics and Biomedicine
Artificial Intelligence in Medicine
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Probabilistic functional integrated networks are powerful tools with which to draw inferences from high-throughput data. However, network analyses are generally not tailored to specific biological functions or processes. This problem may be overcome by extracting process-specific sub-networks, but this approach discards useful information and is of limited use in poorly annotated areas of the network. Here we describe an extension to existing integration methods which exploits dataset biases in order to emphasise interactions relevant to specific processes, without loss of data. We apply the method to high-throughput data for the yeast Saccharomyces cerevisiae, using Gene Ontology annotations for ageing and telomere maintenance as test processes. The resulting networks perform significantly better than unbiased networks for assigning function to unknown genes, and for clustering to identify important sets of interactions. We conclude that this integration method can be used to enhance network analysis with respect to specific processes of biological interest.