TY - JOUR
T1 - Assessment of community efforts to advance network-based prediction of protein–protein interactions
AU - Wang, Xu Wen
AU - Madeddu, Lorenzo
AU - Spirohn, Kerstin
AU - Martini, Leonardo
AU - Fazzone, Adriano
AU - Becchetti, Luca
AU - Wytock, Thomas P.
AU - Kovács, István A.
AU - Balogh, Olivér M.
AU - Benczik, Bettina
AU - Pétervári, Mátyás
AU - Ágg, Bence
AU - Ferdinandy, Péter
AU - Vulliard, Loan
AU - Menche, Jörg
AU - Colonnese, Stefania
AU - Petti, Manuela
AU - Scarano, Gaetano
AU - Cuomo, Francesca
AU - Hao, Tong
AU - Laval, Florent
AU - Willems, Luc
AU - Twizere, Jean Claude
AU - Vidal, Marc
AU - Calderwood, Michael A.
AU - Petrillo, Enrico
AU - Barabási, Albert László
AU - Silverman, Edwin K.
AU - Loscalzo, Joseph
AU - Velardi, Paola
AU - Liu, Yang Yu
N1 - Funding Information:
L.M., A.F., and L.B. were partially supported by the ERC Advanced Grant 788893 AMDROMA “Algorithmic and Mechanism Design Research in Online Markets”, the EC H2020RIA project “SoBigData++” (871042), and the MIUR PRIN project ALGADIMAR “Algorithms, Games, and Digital Markets”. F.L. was supported by a Wallonia-Brussels International (WBI)-World Excellence Fellowship, a Fonds de la Recherche Scientifique (FRS-FNRS)-Télévie Grant (FC31747, Crédit no. 7459421F), a Herman-van Beneden Prize and a Léon Frédéricq Foundation-Josée & Jean Schmets Prize. M.V. is a Chercheur Qualifié Honoraire from the Fonds de la Recherche Scientifique (FRS-FNRS, Wallonia-Brussels Federation, Belgium). M.V acknowledges support from the National Institute of Health (R01 GM130885). P.F. and B.Á. were supported by the National Research, Development and Innovation Office of Hungary (National Heart Program NVKP 16-1-2016-0017) and the Thematic Excellence Programme (2020-4.1.1.-TKP2020) of the Ministry for Innovation and Technology in Hungary, within the framework of the Therapeutic Development and Bioimaging thematic programmes of the Semmelweis University. Project no. RRF-2.3.1-21-2022-00003 has been implemented with the support provided by the European Union. JL acknowledges support from the National Institutes of Health (R01 HL155107, R01 HL155096, U01 HG007690, and U54 HL119145); and from the American Heart Association (D700382 and CV-19). A-LB is supported by the Veteran’s Affairs Medical Center of Boston Contract #36C24122N0769, the NIH grant #1P01HL132825 And the European Union’s Horizon 2020 research and innovation programme under grant agreement No 810115 – DYNASNET. Y.-Y.L. acknowledges grants from National Institutes of Health (R01AI141529, R01HD093761, RF1AG067744, UH3OD023268, U19AI095219, and U01HL089856).
Funding Information:
L.M., A.F., and L.B. were partially supported by the ERC Advanced Grant 788893 AMDROMA “Algorithmic and Mechanism Design Research in Online Markets”, the EC H2020RIA project “SoBigData++” (871042), and the MIUR PRIN project ALGADIMAR “Algorithms, Games, and Digital Markets”. F.L. was supported by a Wallonia-Brussels International (WBI)-World Excellence Fellowship, a Fonds de la Recherche Scientifique (FRS-FNRS)-Télévie Grant (FC31747, Crédit no. 7459421F), a Herman-van Beneden Prize and a Léon Frédéricq Foundation-Josée & Jean Schmets Prize. M.V. is a Chercheur Qualifié Honoraire from the Fonds de la Recherche Scientifique (FRS-FNRS, Wallonia-Brussels Federation, Belgium). M.V acknowledges support from the National Institute of Health (R01 GM130885). P.F. and B.Á. were supported by the National Research, Development and Innovation Office of Hungary (National Heart Program NVKP 16-1-2016-0017) and the Thematic Excellence Programme (2020-4.1.1.-TKP2020) of the Ministry for Innovation and Technology in Hungary, within the framework of the Therapeutic Development and Bioimaging thematic programmes of the Semmelweis University. Project no. RRF-2.3.1-21-2022-00003 has been implemented with the support provided by the European Union. JL acknowledges support from the National Institutes of Health (R01 HL155107, R01 HL155096, U01 HG007690, and U54 HL119145); and from the American Heart Association (D700382 and CV-19). A-LB is supported by the Veteran’s Affairs Medical Center of Boston Contract #36C24122N0769, the NIH grant #1P01HL132825 And the European Union’s Horizon 2020 research and innovation programme under grant agreement No 810115 – DYNASNET. Y.-Y.L. acknowledges grants from National Institutes of Health (R01AI141529, R01HD093761, RF1AG067744, UH3OD023268, U19AI095219, and U01HL089856).
Funding Information:
PF is the founder and CEO of Pharmahungary Group, a group of R&D companies. EKS has received institutional grant support from Bayer and GlaxoSimthKline. A-LB is co-scientific founder of and is supported by Scipher Medicine, Inc., which applies network medicine strategies to biomarker development and personalized drug selection, and is the founder of Naring Inc., which applies data science to health and nutrition. The remaining authors declare no competing interests.
Publisher Copyright:
© 2023, The Author(s).
PY - 2023/12
Y1 - 2023/12
N2 - 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.
AB - 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.
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U2 - 10.1038/s41467-023-37079-7
DO - 10.1038/s41467-023-37079-7
M3 - Article
C2 - 36949045
AN - SCOPUS:85150798089
SN - 2041-1723
VL - 14
JO - Nature communications
JF - Nature communications
IS - 1
M1 - 1582
ER -