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Cited by 400

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Assessing experimentally derived interactions in a small world

Open access
Goldberg, Debra S., Roth, Frederick P.·2003·Proceedings of the National Academy of Sciences

Abstract

Experimentally determined networks are susceptible to errors, yet important inferences can still be drawn from them. Many real networks have also been shown to have the small-world network properties of cohesive neighborhoods and short average distances between vertices. Although much analysis has been done on small-world networks, small-world properties have not previously been used to improve our understanding of individual edges in experimentally derived graphs. Here we focus on a small-world network derived from high-throughput (and error-prone) protein–protein interaction experiments. We exploit the neighborhood cohesiveness property of small-world networks to assess confidence for individual protein–protein interactions. By ascertaining how well each protein–protein interaction (edge) fits the pattern of a small-world network, we stratify even those edges with identical experimental evidence. This result promises to improve the quality of inference from protein–protein interaction networks in particular and small-world networks in general.

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Metadata

Authors
Goldberg, D. S., & Roth, F. P.
Venue
Proceedings of the National Academy of Sciences
Published
2003-04-03
Volume
100
Issue
8
Pages
4372-4376
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2053906518
Cited by
400
References
4
FWCI
8.36
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Assessing experimentally derived interactions in a small world. https://app.cochat.ai/papers/10.1073/pnas.0735871100
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Goldberg, D. S., & Roth, F. P. (2003). Assessing experimentally derived interactions in a small world. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.0735871100

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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