: Comprehensive understanding of the human protein-protein interaction (PPI) network, aka the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Despite the remarkable experimental efforts undertaken to date to determine the structure of the human interactome, many PPIs remain unmapped. Computational approaches, especially network-based methods, can facilitate the identification of previously uncharacterized PPIs. Many such methods have been proposed. Yet, a systematic evaluation of existing network-based methods in predicting PPIs is still lacking. Here, we report community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 26 representative network-based methods to predict PPIs across six different interactomes of four different organisms: A. thaliana, C. elegans, S. cerevisiae, and H. sapiens. Through extensive computational and experimental validations, we found that advanced similarity-based methods, which leverage the underlying network characteristics of PPIs, show superior performance over other general link prediction methods in the interactomes we considered.

Assessment of community efforts to advance network-based prediction of protein-protein interactions / Wang, Xu-Wen; Madeddu, Lorenzo; Spirohn, Kerstin; Martini, Leonardo; Fazzone, Adriano; Becchetti, Luca; Wytock, Thomas P; Kovács, István A; Balogh, Olivér M; Benczik, Bettina; Pétervári, Mátyás; Ágg, Bence; Ferdinandy, Péter; Vulliard, Loan; Menche, Jörg; Colonnese, Stefania; Petti, Manuela; Scarano, Gaetano; Cuomo, Francesca; Hao, Tong; Laval, Florent; Willems, Luc; Twizere, Jean-Claude; Vidal, Marc; Calderwood, Michael A; Petrillo, Enrico; Barabási, Albert-László; Silverman, Edwin K; Loscalzo, Joseph; Velardi, Paola; Liu, Yang-Yu. - In: NATURE COMMUNICATIONS. - ISSN 2041-1723. - 14:1(2023). [10.1038/s41467-023-37079-7]

Assessment of community efforts to advance network-based prediction of protein-protein interactions

Madeddu, Lorenzo;Martini, Leonardo;Fazzone, Adriano;Becchetti, Luca;Colonnese, Stefania;Petti, Manuela;Scarano, Gaetano;Cuomo, Francesca;Petrillo, Enrico;Loscalzo, Joseph;Velardi, Paola
Project Administration
;
2023

Abstract

: Comprehensive understanding of the human protein-protein interaction (PPI) network, aka the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Despite the remarkable experimental efforts undertaken to date to determine the structure of the human interactome, many PPIs remain unmapped. Computational approaches, especially network-based methods, can facilitate the identification of previously uncharacterized PPIs. Many such methods have been proposed. Yet, a systematic evaluation of existing network-based methods in predicting PPIs is still lacking. Here, we report community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 26 representative network-based methods to predict PPIs across six different interactomes of four different organisms: A. thaliana, C. elegans, S. cerevisiae, and H. sapiens. Through extensive computational and experimental validations, we found that advanced similarity-based methods, which leverage the underlying network characteristics of PPIs, show superior performance over other general link prediction methods in the interactomes we considered.
2023
network medicine; link prediction algorithms; protein-protein interaction
01 Pubblicazione su rivista::01a Articolo in rivista
Assessment of community efforts to advance network-based prediction of protein-protein interactions / Wang, Xu-Wen; Madeddu, Lorenzo; Spirohn, Kerstin; Martini, Leonardo; Fazzone, Adriano; Becchetti, Luca; Wytock, Thomas P; Kovács, István A; Balogh, Olivér M; Benczik, Bettina; Pétervári, Mátyás; Ágg, Bence; Ferdinandy, Péter; Vulliard, Loan; Menche, Jörg; Colonnese, Stefania; Petti, Manuela; Scarano, Gaetano; Cuomo, Francesca; Hao, Tong; Laval, Florent; Willems, Luc; Twizere, Jean-Claude; Vidal, Marc; Calderwood, Michael A; Petrillo, Enrico; Barabási, Albert-László; Silverman, Edwin K; Loscalzo, Joseph; Velardi, Paola; Liu, Yang-Yu. - In: NATURE COMMUNICATIONS. - ISSN 2041-1723. - 14:1(2023). [10.1038/s41467-023-37079-7]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1675516
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