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- W2123643026 abstract "What type of connectivity structure are we seeing in protein–protein interaction networks? A number of random graph models have been mooted. After fitting model parameters to real data, the models can be judged by their success in reproducing key network properties. Here, we propose a very simple random graph model that inserts a connection according to the degree, or ‘stickiness’, of the two proteins involved. This model can be regarded as a testable distillation of more sophisticated versions that attempt to account for the presence of interaction surfaces or binding domains. By computing a range of network similarity measures, including relative graphlet frequency distance, we find that our model outperforms other random graph classes. In particular, we show that given the underlying degree information, fitting a stickiness model produces better results than simply choosing a degree-matching graph uniformly at random. Therefore, the results lend support to the basic modelling methodology." @default.
- W2123643026 created "2016-06-24" @default.
- W2123643026 creator A5058486589 @default.
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- W2123643026 date "2006-08-22" @default.
- W2123643026 modified "2023-09-28" @default.
- W2123643026 title "Modelling protein–protein interaction networks via a stickiness index" @default.
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- W2123643026 doi "https://doi.org/10.1098/rsif.2006.0147" @default.
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