ArticleBMC systems biology2013
A novel function prediction approach using protein overlap networks.
Article in BMC systems biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Topology-aware functional similarity: integrating extended neighborhoods via exponential attenuation.BMC bioinformatics · 2025Article
- Protein function prediction using GO similarity-based heterogeneous network propagation.Scientific reports · 2025Article
- The impact of online games on creativity and the role of imagination.Frontiers in behavioral neuroscience · 2025Article
- Protein function annotation based on heterogeneous biological networks.BMC bioinformatics · 2022Article
- A tensor-based bi-random walks model for protein function prediction.BMC bioinformatics · 2022Article
- NPF:network propagation for protein function prediction.BMC bioinformatics · 2020Article
- Article
- Gene Mining for Proline Based Signaling Proteins in Cell Wall ofFrontiers in plant science · 2017Review
- An efficient method for protein function annotation based on multilayer protein networks.Human genomics · 2016Article
- Using multi-instance hierarchical clustering learning system to predict yeast gene function.PloS one · 2014Article
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5 authors.
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Abstract
backgroundConstruction of a reliable network remains the bottleneck for network-based protein function prediction. We built an artificial network model called protein overlap network (PON) for the entire genome of yeast, fly, worm, and human, respectively. Each node of the network represents a protein, and two proteins are connected if they share a domain according to InterPro database.
resultsThe function of a protein can be predicted by counting the occurrence frequency of GO (gene ontology) terms associated with domains of direct neighbors. The average success rate and coverage were 34.3% and 43.9%, respectively, for the test genomes, and were increased to 37.9% and 51.3% when a composite PON of the four species was used for the prediction. As a comparison, the success rate was 7.0% in the random control procedure. We also made predictions with GO term annotations of the second layer nodes using the composite network and obtained an impressive success rate (>30%) and coverage (>30%), even for small genomes. Further improvement was achieved by statistical analysis of manually annotated GO terms for each neighboring protein.
conclusionsThe PONs are composed of dense modules accompanied by a few long distance connections. Based on the PONs, we developed multiple approaches effective for protein function prediction.
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