ArticleProteome science2011
Ontology integration to identify protein complex in protein interaction networks.
Article in Proteome science, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Identifying Protein Complexes from Dynamic Temporal Interval Protein-Protein Interaction Networks.BioMed research international · 2019Article
- Reconstruction of the Protein-Protein Interaction Network for Protein Complexes Identification by Walking on the Protein Pair Fingerprints Similarity Network.Frontiers in genetics · 2018Article
- An effective approach to detecting both small and large complexes from protein-protein interaction networks.BMC bioinformatics · 2017Article
- DIGNiFI: Discovering causative genes for orphan diseases using protein-protein interaction networks.BMC systems biology · 2017Article
- Detecting Protein Complexes in Protein Interaction Networks Modeled as Gene Expression Biclusters.PloS one · 2015Article
- Protein complex identification by integrating protein-protein interaction evidence from multiple sources.PloS one · 2013Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundProtein complexes can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of protein complexes detection algorithms.
methodsWe have developed novel semantic similarity method, which use Gene Ontology (GO) annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. Following the approach of that of the previously proposed clustering algorithm IPCA which expands clusters starting from seeded vertices, we present a clustering algorithm OIIP based on the new weighted Protein-Protein interaction networks for identifying protein complexes.
resultsThe algorithm OIIP is applied to the protein interaction network of Sacchromyces cerevisiae and identifies many well known complexes. Experimental results show that the algorithm OIIP has higher F-measure and accuracy compared to other competing approaches.
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Registered trials
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