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On the structure of proten-protein interaction networks

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Alun Thomas, Rob Cannings, Nicholas A.M. Monk, Chris Cannings

Molecular Networks · Submitted 23 Sep 2003

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Our results show that the observation of approximate power law behaviour in a sampled sub-graph does not imply that the underlying graph follows a power law.

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arXiv IDq-bio/0309012 (version 1)
Pith IDLSPBDAXX, Pith's permanent record of this paper and its review
CitationAlun Thomas, Chris Cannings, Nicholas A.M. Monk, Rob Cannings. "On the structure of proten-protein interaction networks." arXiv:q-bio/0309012 (2003). https://pith.science/paper/LSPBDAXX
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,357 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.40 · human participants 0.06 · controlled comparison 0.20 · randomized assignment 0.01 · states quantitative result 0.12 · compares with prior results 0.94 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

We present a simple model for the underlying structure of protein-protein pairwise interaction graphs that is based on the way in which proteins attach to each other in experiments such as yeast two-hybrid assays. We show that data on the interactions of human proteins lend support to this model. The frequency of the number of connections per protein under this model does not follow a power law, in contrast to the reported behaviour of data from large scale yeast two-hybrid screens of yeast protein-protein interactions. Sampling sub-graphs from the underlying graphs generated with our model, in a way analogous to the sampling performed in large scale yeast two-hybrid searches, gives degree distributions that differ subtly from the power law and that fit the observed data better than the power law itself. Our results show that the observation of approximate power law behaviour in a sampled sub-graph does not imply that the underlying graph follows a power law.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We present a simple model for the underlying structure of protein-protein pairwise interaction graphs that is based on the way in which proteins attach to each other in experiments such as yeast two-hybrid assays. contribution 0.70 0.87 0.06 asserted 0.99
s1 We show that data on the interactions of human proteins lend support to this model. result 0.94 0.88 0.09 asserted 0.96
s2 The frequency of the number of connections per protein under this model does not follow a power law, in contrast to the reported behaviour of data from large scale yeast two-hybrid screens of yeast protein-protein interactions. result 1.00 0.72 0.20 asserted 1.00
s3 Sampling sub-graphs from the underlying graphs generated with our model, in a way analogous to the sampling performed in large scale yeast two-hybrid searches, gives degree distributions that differ subtly from the power law and that fit the observed data better than the power law itself. result 0.96 0.95 0.17 asserted 1.00
s4 Our results show that the observation of approximate power law behaviour in a sampled sub-graph does not imply that the underlying graph follows a power law. interpretation 0.99 0.87 0.79 asserted 1.00

What kind of paper the classifier reports this is: theoretical result 0.94 · evidence from analysis of existing data 0.75.

What was examined, and what was not

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  • references and citing sentences
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  • P abstract
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  • S sections
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  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

We present a simple model for the underlying structure of protein-protein pairwise interaction graphs that is based on the way in which proteins attach to each other in experiments such as yeast two-hybrid assays. We show that data on the interactions of human proteins lend support to this model. The frequency of the number of connections per protein under this model does not follow a power law, in contrast to the reported behaviour of data from large scale yeast two-hybrid screens of yeast protein-protein interactions. Sampling sub-graphs from the underlying graphs generated with our model, in a way analogous to the sampling performed in large scale yeast two-hybrid searches, gives degree distributions that differ subtly from the power law and that fit the observed data better than the power law itself. Our results show that the observation of approximate power law behaviour in a sampled sub-graph does not imply that the underlying graph follows a power law.

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