C
Charles E. Chapple
Researcher at Aix-Marseille University
Publications - 6
Citations - 297
Charles E. Chapple is an academic researcher from Aix-Marseille University. The author has contributed to research in topics: Protein moonlighting & Clustering coefficient. The author has an hindex of 6, co-authored 6 publications receiving 280 citations. Previous affiliations of Charles E. Chapple include Centre national de la recherche scientifique & French Institute of Health and Medical Research.
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Journal ArticleDOI
Multifunctional proteins revealed by overlapping clustering in protein interaction network
TL;DR: Overlapping Cluster Generator (OCG), a novel clustering method which decomposes a network into overlapping clusters and which is, therefore, capable of correct assignment of multifunctional proteins, is presented.
Journal ArticleDOI
Extreme multifunctional proteins identified from a human protein interaction network.
Charles E. Chapple,Benoît Robisson,Benoît Robisson,Lionel Spinelli,Celine Guien,Celine Guien,Emmanuelle Becker,Emmanuelle Becker,Emmanuelle Becker,Christine Brun +9 more
TL;DR: This work proposes the first method for the identification of ‘extreme multifunctional' proteins from an interactome as a first step to characterize moonlighting proteins, and shows that the candidates form a distinct sub-group of proteins, characterized by specific features, which form a signature of extreme multifunctionality.
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Clust&See: a Cytoscape plugin for the identification, visualization and manipulation of network clusters.
Lionel Spinelli,Philippe Gambette,Charles E. Chapple,Benoît Robisson,Anaïs Baudot,Henri Garreta,Laurent Tichit,Alain Guénoche,Christine Brun +8 more
TL;DR: Clust&See, a Cytoscape plugin dedicated to the identification, visualization and analysis of clusters extracted from networks, provides the ability to apply three different, recently developed graph clustering algorithms to networks and to visualize the obtained partition as a quotient graph.
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Redefining protein moonlighting
TL;DR: This work presented the first computational method for the large-scale identification of what they have termed “extreme multifunctional proteins” (EMF) - proteins whose multiple functions are very dissimilar to one another, and developed two measures of functional dissimilarity based on the frequency of interactions between proteins performing each function.
Journal ArticleDOI
Relationships between predicted moonlighting proteins, human diseases, and comorbidities from a network perspective
Andreas Zanzoni,Andreas Zanzoni,Charles E. Chapple,Charles E. Chapple,Christine Brun,Christine Brun +5 more
TL;DR: The results suggest that some observed comorbidities between phenotypically different diseases could be due to a shared protein involved in unrelated biological processes.