Protein interaction networks from yeast to human.
By Peer Bork, Lars Juhl Jensen, C. von Mering, Arun K. Ramani, Insuk Lee, Edward M. Marcotte. Current Opinion in Structural Biology, 2004
374 citations recorded by the source corpus.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/ae5d64fd761e7e91451a213074ec3be62fdfb34c
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Citations in the corpus
- A human protein-protein interaction network: a resource for annotating the proteome. (2005, positive): “Nevertheless, the transfer of interaction information from model organisms to human is far more difficult than anticipated (Bork et al., 2004; Ramani et al., 2005).”
- When individual behaviour matters: homogeneous and network models in epidemiology (2007, background): “the Internet, the World Wide Web) and biological systems (e.g. metabolic, protein interactions, transcription regulation and protein domain; Albert et al. 1999; Faloutsos et al. 1999;Wuchty 2001; Giot et al. 2003; Bork et al. 2004; Hatzimanikatis et al. 2004; Luscombe et al. 2004).”
- HIPPIE: Integrating Protein Interaction Networks with Experiment Based Quality Scores (2012, background): “For comparison, the complete human interactome map has been estimated to contain between 200 000 and 400 000 interactions (according to [32] and [33], respectively) suggesting that our knowledge of the human interactome is still incomplete.”
- Flexible nets (2005, result): “This has been illustrated by studies comparing the results from various large-scale efforts [15– 18].”
- Graph theory and networks in Biology. (2006, background): “In particular, they has expected that the degrees of most nodes would be close to the mean degree, 〈k〉, of the network, and that P (k) would decay exponentially as |k−〈k〉| increased.”
- Flexible nets: disorder and induced fit in the associations of p53 and 14-3-3 with their partners (2008, background): “Currently constructed networks are noisy, with both false positive and false negative interactions [8,1820].”
- Accurate prediction of protein–protein interactions from sequence alignments using a Bayesian method (2008, methodology): “(1) Here n is the total number receivers of class c that have an amino acid α in column i of the alignment (gaps are treated as a 21st amino acid) and λ is the pseudo-count resulting from the Dirichlet prior (we used the Jeffreys’ prior λ = 1 / 2). w is thus the estimated probability of seeing amino…”
- Interactome: gateway into systems biology. (2005, background): “Although only a fraction of the expected number of interactions for any organism examined to date have been identified (74,124,125), the proteome-scale screens done so far have nevertheless connected hundreds of uncharacterized proteins to well-characterized partners (52,126–128), thereby circumvent”
- Recent progress and future directions in protein-protein docking. (2008, neutral): “1389-2037/08 $55.00+.00 © 2008 Bentham Science Publishers Ltd. Keywords: Protein-protein docking, protein-protein interactions, docking algorithms, data-driven docking, molecular dynamics, protein structure databases, protein interface prediction, CAPRI.”
- Traversing the conceptual divide between biological and statistical epistasis: systems biology and a more modern synthesis. (2005, background): “It has been estimated that there are roughly 10,000–30,000 pairwise interactions among yeast proteins and perhaps as many as 200,000 or more in humans.((21)) Despite the daunting task, it is clear that the detection and characterization of biological epistasis is dramatically improving with technolo”
- Algorithmic Transparency via Quantitative Input Influence: Theory and Experiments with Learning Systems (2016, methodology): “[60] use the Shapley value to assign importance to protein interactions in large, complex biological interaction networks; Keinan et al.”
- The BioGRID Interaction Database: 2011 update (2010, background): “Cellular behavior is dictated by a complex global network of protein interactions, which are modified at the level of gene expression, messenger RNA (mRNA) stability, translation rate, degradation, localization and post-translational modifications (1).”