Functional classification of proteins for the prediction of cellular function from a protein-protein interaction network

By C. Brun, François Chevenet, David Martin, J. Wojcik, A. Guénoche, Bernard Jacq. Genome Biology, 2003

295 citations recorded by the source corpus.

We here describe PRODISTIN, a new computational method allowing the functional clustering of proteins on the basis of protein-protein interaction data. This method, assessed biologically and statistically, enabled us to classify 11% of the Saccharomyces cerevisiae proteome into several groups, the majority of which contained proteins involved in the same biological process(es), and to predict a cellular function for many otherwise uncharacterized proteins.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/175ce1b127af3c6b79197229366e29dd223db154

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Citations in the corpus

TreeDyn: towards dynamic graphics and annotations for analyses of trees (2006, background): “For instance, a "protein tree" is automatically colored according to 'Cellular role' annotations from Yeast Proteome Database [19]. having identical values for a given variable ( e.g . in a phylogenetic study of host-parasite co-speciation, a host tree is colored according to parasites) is possible.”
Network-based prediction of protein function (2007, methodology): “Additional similarity measures proposed for using in a hierarchical clustering setting include Czekanovski–Dice distance (Brun et al , 2003) and the statistical signiﬁcance of the number of common interaction partners (Samanta and Liang, 2003).”
GOToolBox: functional analysis of gene datasets based on Gene Ontology (2004, methodology): “The Czekanowski-Dice distance and the corresponding clustering have already proved their effectiveness in delineating protein functional classes derived from the analysis of protein-protein interaction graphs [10].”
Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding (2013, methodology): “, 2005), or to predict protein function, such as the Czekanowski-Dice Dissimilarity (CDD) (Brun et al., 2003) and FSW (Chua et al., 2006). These techniques have also been used to predict PPIs based on the topological properties of the neighbours of candidate protein pairs (You et al., 2010). As show”
Protein interaction networks from yeast to human. (2004, background): “Implicitly, modules are also the basis of approaches that functionally classify proteins according to their network neighbors [64–66].”
From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks (2013, methodology): “…evidences in experimental protein interaction detection; the Czekanowski-Dice Dissimilarity (CDD, as well as its adjusted version ACDD) 31,32 and the Functional Similarity Weight (FSW) 33 stem from methods for protein functional prediction in interactomes; and ISOMAP (ISO) 29,34 is…”
In silico prediction of protein-protein interactions in human macrophages (2001, methodology): “The similarity index corresponds to Czekanowski-Dice formula [13,46].”
Evaluation of clustering algorithms for protein-protein interaction networks (2006, unknown): “[18-34]).”