Network propagation: a universal amplifier of genetic associations

By Lenore J. Cowen, Trey Ideker, Benjamin J. Raphael, Roded Sharan. Nature reviews genetics, 2017

610 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ec094126209a443112cf751ae438f9b9705f348c

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Citations in the corpus

Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities (2018, background): “Fundamental to biological networks is the principle that genes involved in the same cellular function or underlying the same phenotype tend to interact [49].”
Assessment of network module identification across complex diseases (2019, neutral): “, by projecting known disease genes onto the network and finding modules that span these genes (Cowen et al., 2017).”
Open Graph Benchmark: Datasets for Machine Learning on Graphs (2020, background): “…over time (Sharan et al., 2005; Zitnik et al., 2019), the discovery of functional associations between genes even for otherwise rarely-studied organisms (Cowen et al., 2017), and would give us insights into key bioinformatics tasks, such as biological network alignment (Malod-Dognin et al., 2017).”
Mutation severity spectrum of rare alleles in the human genome is predictive of disease type (2019, background): “However, disease gene prioritization and disease-causing variation discovery are still difficult [19, 20].”
Open Community Challenge Reveals Molecular Network Modules with Key Roles in Diseases (2018, background): “In contrast, supervised or integrative network analysis methods rely on additional biological data from the start, e.g., by projecting known disease genes onto the network and finding modules that span these genes (Cowen et al., 2017).”
Integrative omics for health and disease (2018, background): “Specifically, network models, including protein–protein interaction, regulatory and co-expression networks, have proved to be a valuable resource for prioritizing and identifying disease genes and pathways (for recent reviews, see REFS 23–26 ).”
The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets (2020, background): “They enable the inference of molecular functions through the ‘guilt-by-association’ principle (4,5), allow the characterization of modularity in biological processes (6–8) and serve as substrates for deep learning (9,10).”
The genomics of local adaptation in trees: are we out of the woods yet? (2017, background): “Other areas amenable to network description such as metabolomics and proteomics would also be a complement (Feltus 2014; Cowen et al. 2017), particularly if genetic studies contextualize results with findings from such approaches and vice versa.”
Graph embedding on biomedical networks: methods, applications and evaluations (2019, neutral): “In the recent biomedical network analyses, a very popular paradigm is network propagation (Cowen et al., 2017), which amplifies a biological signal (e.”