Assessing experimentally derived interactions in a small world

By Debra S. Goldberg, Frederick P. Roth. Proceedings of the National Academy of Sciences of the United States of America, 2003

432 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/8aa01ac5331836245ee8fd3c4f65df8530854597

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Citations in the corpus

Toward a Comprehensive Atlas of the Physical Interactome of Saccharomyces cerevisiae*S (2007, background): “The use of indirect prey-prey information was also a component of the socio-affinity score, and it is conceptually related to a computational approach taken to predict PPIs based on shared interaction partners (22).”
Pathway Commons, a web resource for biological pathway data (2010, methodology): “In the future, Pathway Commons will implement published algorithms (11,12) Table 1.”
Global and Local Architecture of the Mammalian microRNA–Transcription Factor Regulatory Network (2007, methodology): “We chose the Meet/Min score [29,30], which is formulated in the main text, and calculated it on the matrix of miR to target genes.”
Angular Synchronization by Eigenvectors and Semidefinite Programming (2009, positive): “are in Eforming a triangle (i,j,k). On the other hand, shortcut edges are not expected to be sides of as many triangles. This cliquishness property of small-world graphs was used by Goldberg and Roth [14] to denoise protein-protein interaction maps by thresholding edges that appear in only a few tri”
A human protein-protein interaction network: a resource for annotating the proteome. (2005, positive): “Besides the clustering coefficient, the topological coefficient was used (Goldberg and Roth, 2003; Ravasz et al., 2002) to study the characteristics of the interaction network.”
Network-based prediction of protein function (2007, methodology): “The authors used a special weighted form of mutual clustering coefficient (Goldberg and Roth, 2003) for quantifying similarities between proteins, and a modified version of the k-means algorithm (Hartigan, 1975) was used for cluster detection. Arnau et al (2005) used the shortest path length between”
Protein interaction networks from yeast to human. (2004, background): “However, highquality subsets could often be chosen on the basis of additional criteria [15 ,16 ,38,39], such as the degree to which mRNAs of interacting proteins are co-expressed in microarray experiments [40,41], topological properties of the resulting networks [42,43], shared pathways or subcellul”
Link prediction approach to collaborative filtering (2005, background): “Link prediction has been an important problem in network modeling and has recently been studied in social network, genetic interaction network, and literature citation network contexts [2, 5, 6].”
Gaining confidence in high-throughput protein interaction networks (2004, methodology): “Topological measures of clustering have been applied to Y2H dat”
Network-based prediction of protein interactions (2018, methodology): “S8) and the Preferential Attachment principle (PA) [18, 19], a link prediction method that mimics unspecific binding between ”sticky” proteins by placing a link between two nodes with a score given by the”
Efficient Algorithms for Detecting Signaling Pathways in Protein Interaction Networks (2005, methodology): “…coefficient of expression measurements for the corresponding genes (using 794 expression profiles obtained from the Stanford Microarray Database (Gollub et al., 2003 )); and (3) the proteins' small world clustering coefficient (Goldberg and Roth, 2003), which is defined as minus the logarithm…”
Gene network interconnectedness and the generalized topological overlap measure (2007, background): “Several measures that keep track of shared 1 step neighbors have been proposed in the literature, e.g. [28].”