Detecting overlapping protein complexes in protein-protein interaction networks
By Tamás Nepusz, Haiyuan Yu, Alberto Paccanaro. Nature Methods, 2012
1,237 citations recorded by the source corpus.
We introduce clustering with overlapping neighborhood expansion (ClusterONE), a method for detecting potentially overlapping protein complexes from protein-protein interaction data. ClusterONE-derived complexes for several yeast data sets showed better correspondence with reference complexes in the Munich Information Center for Protein Sequence (MIPS) catalog and complexes derived from the Saccharomyces Genome Database (SGD) than the results of seven popular methods. The results also showed a high extent of functional homogeneity.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/ec41d74bebb9fe18d3939967ad752b5630abed92
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Molecular Biology
- Jishnu Das
- Gabriel C. Wu
- Minoru Yoshida
- Haiyuan Yu
- Francisco Javier Azuaje
- Edward M. Marcotte
- Tamás Nepusz
- Elisabeth R. M. Tillier
Citations in the corpus
- Hierarchical cooperation of transcription factors from integration analysis of DNA sequences, ChIP-Seq and ChIA-PET data (2019, methodology): “A community of PPI is a candidate functional module [29, 30].”
- A proteome-wide fission yeast interactome reveals network evolution principles from yeasts to human (2016, methodology): “First, we used ClusterOne (Nepusz et al., 2012) to detect topological protein clusters in FissionNet (see Supplemental Experimental Procedures).”
- A Census of Human Soluble Protein Complexes (2012, positive): “Construction and Validation of Protein Complexes from the Probabilistic Interaction Network In order to define complex membership, we partitioned the highconfidence probabilistic physical interaction network by using the cluster growth algorithm ClusterONE (Nepusz et al., 2012), which outperformed o”
- Linking Biochemical Pathways and Networks to Adverse Drug Reactions (2014, methodology): “Next we applied a recently published network clustering algorithm, ClusterOne [27], to extract potentially significant modules of pathway-ADR associations.”