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István Kovács
Researcher at Northwestern University
Publications - 178
Citations - 5090
István Kovács is an academic researcher from Northwestern University. The author has contributed to research in topics: Automorphism & Cayley graph. The author has an hindex of 29, co-authored 157 publications receiving 3779 citations. Previous affiliations of István Kovács include Eötvös Loránd University & Harvard University.
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A reference map of the human binary protein interactome
Katja Luck,Dae-Kyum Kim,Luke Lambourne,Kerstin Spirohn,Bridget E. Begg,Wenting Bian,Ruth Brignall,Tiziana M. Cafarelli,Francisco J. Campos-Laborie,Benoit Charloteaux,Dong-Sic Choi,Atina G. Cote,Meaghan Daley,Steven Deimling,Alice Desbuleux,Amélie Dricot,Marinella Gebbia,Madeleine F. Hardy,Nishka Kishore,Jennifer J. Knapp,István Kovács,István Kovács,Irma Lemmens,Irma Lemmens,Miles W. Mee,Joseph C. Mellor,Carl Pollis,Carles Pons,Aaron Richardson,Sadie Schlabach,Bridget Teeking,Anupama Yadav,Mariana Babor,Dawit Balcha,Omer Basha,Christian Bowman-Colin,Suet-Feung Chin,Soon Gang Choi,Claudia Colabella,Georges Coppin,Cassandra D’Amata,David De Ridder,Steffi De Rouck,Steffi De Rouck,Miquel Duran-Frigola,Hanane Ennajdaoui,Florian Goebels,Liana Goehring,Anjali Gopal,Ghazal Haddad,Elodie Hatchi,Mohamed Helmy,Yves Jacob,Yves Jacob,Yoseph Kassa,Serena Landini,Roujia Li,Natascha van Lieshout,Andrew MacWilliams,Dylan Markey,Joseph N. Paulson,Joseph N. Paulson,Sudharshan Rangarajan,John Rasla,Ashyad Rayhan,Thomas Rolland,Adriana San-Miguel,Yun Shen,Dayag Sheykhkarimli,Gloria M. Sheynkman,Eyal Simonovsky,Murat Tasan,Alexander O. Tejeda,Vincent Tropepe,Jean-Claude Twizere,Yang Wang,Robert J. Weatheritt,Jochen Weile,Yu Xia,Yu Xia,Xinping Yang,Esti Yeger-Lotem,Quan Zhong,Patrick Aloy,Gary D. Bader,Javier De Las Rivas,Suzanne Gaudet,Tong Hao,Janusz Rak,Jan Tavernier,Jan Tavernier,David E. Hill,Marc Vidal,Frederick P. Roth,Michael A. Calderwood +94 more
TL;DR: The utility of HuRI is demonstrated in identifying the specific subcellular roles of protein–protein interactions and in identifying potential molecular mechanisms that might underlie tissue-specific phenotypes of Mendelian diseases.
Journal ArticleDOI
Widespread Macromolecular Interaction Perturbations in Human Genetic Disorders
Nidhi Sahni,Song Yi,Mikko Taipale,Juan I. Fuxman Bass,Jasmin Coulombe-Huntington,Fan Yang,Fan Yang,Jian Peng,Jochen Weile,Jochen Weile,Georgios I. Karras,Yang Wang,István Kovács,István Kovács,Atanas Kamburov,Irina Krykbaeva,Mandy H. Y. Lam,George Tucker,Vikram Khurana,Amitabh Sharma,Amitabh Sharma,Yang-Yu Liu,Yang-Yu Liu,Nozomu Yachie,Nozomu Yachie,Quan Zhong,Yun Shen,Alexandre Palagi,Adriana San-Miguel,Changyu Fan,Dawit Balcha,Amélie Dricot,Daniel M. Jordan,Jennifer M. Walsh,Akash A. Shah,Xinping Yang,Ani K. Stoyanova,Alex Leighton,Michael A. Calderwood,Yves Jacob,Yves Jacob,Michael E. Cusick,Kourosh Salehi-Ashtiani,Luke Whitesell,Shamil R. Sunyaev,Shamil R. Sunyaev,Bonnie Berger,Albert-László Barabási,Albert-László Barabási,Benoit Charloteaux,David E. Hill,Tong Hao,Frederick P. Roth,Frederick P. Roth,Frederick P. Roth,Yu Xia,Yu Xia,Albertha J.M. Walhout,Albertha J.M. Walhout,Susan Lindquist,Susan Lindquist,Marc Vidal +61 more
TL;DR: This work functionally profile several thousand missense mutations across a spectrum of Mendelian disorders using various interaction assays, suggesting that disease-associated alleles that perturb distinct protein activities rather than grossly affecting folding and stability are relatively widespread.
Journal ArticleDOI
Network-based prediction of drug combinations.
TL;DR: A network-based methodology to identify efficacious drug combinations for specific diseases is proposed, and it is found that successful combinations tend to target separate neighbourhoods of the disease module in the human interactome.
Journal ArticleDOI
Community Landscapes: An Integrative Approach to Determine Overlapping Network Module Hierarchy, Identify Key Nodes and Predict Network Dynamics
TL;DR: The novel concept of ModuLand is introduced, an integrative method family determining overlapping network modules as hills of an influence function-based, centrality-type community landscape, and including several widely used modularization methods as special cases.
Journal ArticleDOI
Network-based prediction of protein interactions.
István Kovács,Katja Luck,Kerstin Spirohn,Yang Wang,Carl Pollis,Sadie Schlabach,Wenting Bian,Dae-Kyum Kim,Dae-Kyum Kim,Nishka Kishore,Nishka Kishore,Tong Hao,Michael A. Calderwood,Marc Vidal,Albert-László Barabási +14 more
TL;DR: It is shown that proteins tend to interact if one is similar to the other’s partners and that PPI prediction based on this principle is highly accurate and can offer mechanistic insights into disease mechanisms and complement future experimental efforts to complete the human interactome.