ArticleBMC bioinformatics2004
Clustering proteins from interaction networks for the prediction of cellular functions.
Article in BMC bioinformatics, 2004. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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25 citing papers in PubMed, 122 citations in OpenAlex.
- A Seed Expansion Graph Clustering Method for Protein Complexes Detection in Protein Interaction Networks.Molecules (Basel, Switzerland) · 2017Article
- Identifying new susceptibility genes on dopaminergic and serotonergic pathways for the framing effect in decision-making.Social cognitive and affective neuroscience · 2017Article
- AtPID: a genome-scale resource for genotype-phenotype associations in Arabidopsis.Nucleic acids research · 2017Article
- An integrated quantitative proteomics and systems biology approach to explore synaptic protein profile changes during morphine exposure.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2014Review
- Graphlet-based edge clustering reveals pathogen-interacting proteins.Bioinformatics (Oxford, England) · 2012Article
- Efficient and accurate Greedy Search Methods for mining functional modules in protein interaction networks.BMC bioinformatics · 2012Article
- Accuracy improvement in protein complex prediction from protein interaction networks by refining cluster overlaps.Proteome science · 2012Article
- Multifunctional proteins revealed by overlapping clustering in protein interaction network.Bioinformatics (Oxford, England) · 2012Article
- Prior knowledge based mining functional modules from Yeast PPI networks with gene ontology.BMC bioinformatics · 2010Article
- Article
- Functional enrichment analyses and construction of functional similarity networks with high confidence function prediction by PFP.BMC bioinformatics · 2010Article
- Identification of functional hubs and modules by converting interactome networks into hierarchical ordering of proteins.BMC bioinformatics · 2010Article
- Systems-level cancer gene identification from protein interaction network topology applied to melanogenesis-related functional genomics data.Journal of the Royal Society, Interface · 2010Article
- POLAR MAPPER: a computational tool for integrated visualization of protein interaction networks and mRNA expression data.Journal of the Royal Society, Interface · 2009Article
- Gene module level analysis: identification to networks and dynamics.Current opinion in biotechnology · 2008Review
- Defining a modular signalling network from the fly interactome.BMC systems biology · 2008Article
- Uncovering biological network function via graphlet degree signatures.Cancer informatics · 2008Article
- Semantic integration to identify overlapping functional modules in protein interaction networks.BMC bioinformatics · 2007Article
- Evaluation of clustering algorithms for protein-protein interaction networks.BMC bioinformatics · 2006Article
- Similarity searches in genome-wide numerical data sets.Biology direct · 2006Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDeveloping reliable and efficient strategies allowing to infer a function to yet uncharacterized proteins based on interaction networks is of crucial interest in the current context of high-throughput data generation. In this paper, we develop a new algorithm for clustering vertices of a protein-protein interaction network using a density function, providing disjoint classes.
resultsApplied to the yeast interaction network, the classes obtained appear to be biological significant. The partitions are then used to make functional predictions for uncharacterized yeast proteins, using an annotation procedure that takes into account the binary interactions between proteins inside the classes. We show that this procedure is able to enhance the performances with respect to previous approaches. Finally, we propose a new annotation for 37 previously uncharacterized yeast proteins.
conclusionWe believe that our results represent a significant improvement for the inference of cellular functions, that can be applied to other organism as well as to other type of interaction graph, such as genetic interactions.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.