Literature DB >> 30850642

Collective influencers in protein interaction networks.

T A Boltz1, P Devkota1, Stefan Wuchty2,3,4,5.   

Abstract

Recent research increasingly shows the relevance of network based approaches for our understanding of biological systems. Analyzing human protein interaction networks, we determined collective influencers (CI), defined as network nodes that damage the integrity of the underlying networks to the utmost degree. We found that CI proteins were enriched with essential, regulatory, signaling and disease genes as well as drug targets, indicating their biological significance. Also by focusing on different organisms, we found that CI proteins had a penchant to be evolutionarily conserved as CI proteins, indicating the fundamental role that collective influencers in protein interaction networks plays for our understanding of regulation, diseases and evolution.

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Year:  2019        PMID: 30850642      PMCID: PMC6408499          DOI: 10.1038/s41598-019-40410-2

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.379


  47 in total

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Journal:  Nat Genet       Date:  2000-05       Impact factor: 38.330

2.  Evolution and topology in the yeast protein interaction network.

Authors:  Stefan Wuchty
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3.  The human disease network.

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4.  Controllability in protein interaction networks.

Authors:  Stefan Wuchty
Journal:  Proc Natl Acad Sci U S A       Date:  2014-04-28       Impact factor: 11.205

5.  Disease networks. Uncovering disease-disease relationships through the incomplete interactome.

Authors:  Jörg Menche; Amitabh Sharma; Maksim Kitsak; Susan Dina Ghiassian; Marc Vidal; Joseph Loscalzo; Albert-László Barabási
Journal:  Science       Date:  2015-02-20       Impact factor: 47.728

6.  Peeling the yeast protein network.

Authors:  Stefan Wuchty; Eivind Almaas
Journal:  Proteomics       Date:  2005-02       Impact factor: 3.984

Review 7.  A census of human cancer genes.

Authors:  P Andrew Futreal; Lachlan Coin; Mhairi Marshall; Thomas Down; Timothy Hubbard; Richard Wooster; Nazneen Rahman; Michael R Stratton
Journal:  Nat Rev Cancer       Date:  2004-03       Impact factor: 60.716

8.  DrugBank 3.0: a comprehensive resource for 'omics' research on drugs.

Authors:  Craig Knox; Vivian Law; Timothy Jewison; Philip Liu; Son Ly; Alex Frolkis; Allison Pon; Kelly Banco; Christine Mak; Vanessa Neveu; Yannick Djoumbou; Roman Eisner; An Chi Guo; David S Wishart
Journal:  Nucleic Acids Res       Date:  2010-11-08       Impact factor: 16.971

9.  DEG 10, an update of the database of essential genes that includes both protein-coding genes and noncoding genomic elements.

Authors:  Hao Luo; Yan Lin; Feng Gao; Chun-Ting Zhang; Ren Zhang
Journal:  Nucleic Acids Res       Date:  2013-11-15       Impact factor: 16.971

10.  The OMA orthology database in 2018: retrieving evolutionary relationships among all domains of life through richer web and programmatic interfaces.

Authors:  Adrian M Altenhoff; Natasha M Glover; Clément-Marie Train; Klara Kaleb; Alex Warwick Vesztrocy; David Dylus; Tarcisio M de Farias; Karina Zile; Charles Stevenson; Jiao Long; Henning Redestig; Gaston H Gonnet; Christophe Dessimoz
Journal:  Nucleic Acids Res       Date:  2018-01-04       Impact factor: 16.971

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  2 in total

Review 1.  Computational Network Inference for Bacterial Interactomics.

Authors:  Katherine James; Jose Muñoz-Muñoz
Journal:  mSystems       Date:  2022-03-30       Impact factor: 7.324

2.  Tracing the footsteps of autophagy in computational biology.

Authors:  Dipanka Tanu Sarmah; Nandadulal Bairagi; Samrat Chatterjee
Journal:  Brief Bioinform       Date:  2021-07-20       Impact factor: 11.622

  2 in total

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