Dynamic modularity in protein interaction networks predicts breast cancer outcome

By I. Taylor, Rune Linding, David Warde-Farley, Yongmei Liu, Catia Pesquita, Daniel Faria, S. Bull, Tony Pawson, Quaid D. Morris, Jeffrey L. Wrana. 2009

748 citations recorded by the source corpus.

Subfields: Cell Biology, Molecular Biology

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Citations in the corpus

EgoNet: identification of human disease ego-network modules (2014, methodology): “To date, many computational methods have been developed to integrate gene expression profiles with protein-protein interaction maps or pathway databases, with the goal of identifying significant subnetwork markers for predicting biological or clinical outcomes [10-18].”
Differential network biology (2012, background): “For example, several groups have shown that alterations in the physical interaction network can be a powerful indicator of breast cancer prognosis (Chuang et al , 2007; Taylor et al , 2009).”
Artificial intelligence in cancer target identification and drug discovery (2022, positive): “For example, Taylor et al. 137 used betweenness centrality analysis to identify intermodular hub proteins and intramodular hub proteins in the breast cancer network.”
An atlas of combinatorial transcriptional regulation in mouse and man (2010, positive): “Following (Taylor et al., 2009) interaction values were computed for each interaction between a hub and any other TF, with hubs taken as TFs with > 12 interactions (Figure 1C, Table S2, and Table S3).”
Integrative approaches for finding modular structure in biological networks (2013, background): “Molecular interactions can change dramatically in response to cellular cues, developmental stages, environmental stresses, pharmacological treatments and disease states 32,100,129,132,133 .”
Studying protein^protein interaction networks: a systems view on diseases (2012, neutral): “[40]) or nodes in particular positions (e.”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, background): “Taylor et al. (2009) showed that altered modularity of hubs had a prognostic value in breast cancer and suggested cancer-specific inter-modular hubs as drug targets in cancer therapies.”
Protein Networks as Logic Functions in Development and Cancer (2011, background): “74 (Figures 4A, S5A), which was better by 3–6% than previously reported results for a variety of standard and network/pathway-based classification methods [24,25,27].”
The BioGRID interaction database: 2015 update (2014, methodology): “With such interaction datasets in hand, it has been possible to implement computational methods for analysis and prediction of the response of cellular networks to perturbation by disease-associated mutation or pathogen infection (28,36–38).”
Structure-based Systems Biology for Analyzing Off-target Binding (2011, methodology): “Network analysis and simulation (the analysis of the dynamics inherent in the network) has proved to be a powerful tool in identifying novel drug targets [102], elucidating mechanisms of drug resistance [103], predicting drug response phenotypes [104–106], and identifying genetic risk factors [106].”
Semantic Similarity in Biomedical Ontologies (2009, methodology): “Also in the context of interactions, semantic similarity has been used to extract functional modules from interaction networks [62], to align biological pathways [63] to generate functionally meaningful network subsets [64], and to characterize protein interaction networks to support breast cancer o”
Network-based methods for human disease gene prediction. (2011, background): “The concept has been employed in the study of various diseases including, but not limited to, different types of cancers, type 2 diabetes, obesity, asthma, neurological diseases and so on [59–63].”