Walking the interactome for prioritization of candidate disease genes.

By Sebastian Köhler, Sebastian Bauer, D. Horn, Peter N. Robinson. American Journal of Human Genetics, 2008

1,242 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/282bf4d64734aff0f9dc430a60611131743acb38

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Citations in the corpus

Mining breast cancer genes with a network based noise-tolerant approach (2013, methodology): “The steps in [13] were followed to perform random walk.”
The power of protein interaction networks for associating genes with diseases (2010, positive): “The random walk methods [RW (Kohler et al., 2008) and Prop (Vanunu and Sharan, 2008)] show a clear dominance over the clustering and neighborhood methods. The similar performance of RW and Prop is not surprising because the prior-evidence vector of Vanunu and Sharan (2008) is similar in principle to”
STRING 8—a global view on proteins and their functional interactions in 630 organisms (2008, background): “Nevertheless, protein–protein interaction networks (or also ‘association networks’ in case functional associations are included) are a crucial ingredient for any system-level understanding of cellular machineries (1–5).”
EnrichNet: network-based gene set enrichment analysis (2012, background): “Both the datasets that share none of their genes/proteins with a pathway of interest (overlap size is zero) and cannot be scored with the over-representation approach (see right margin in Fig.”
Review of Drug Repositioning Approaches and Resources (2018, background): “Different biological entities included in the heterogeneous networks not only provide an opportunity to improve the performance of existing methods but also offer a tool to design more efficient and stable approaches [24, 26, 27, 29, 31, 55, 58] (see table 1).”
Next-generation diagnostics and disease-gene discovery with the Exomiser (2015, methodology): “Finally, for genes that have no phenotype data from any of these sources, we use a random walk with restart algorithm 20 a protein-protein association network to genes with strong phenotypic similarity to the patient.”