Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Motivation: Much of the large-scale molecular data from living cells can be represented in terms of networks. Such networks occupy a central position in cellular systems biology. In the protein–protein interaction (PPI) network, nodes represent proteins and edges represent connections beten them, based on experimental evidence. As PPI networks are rich and complex, a mathematical model is sought to capture their properties and shed light on PPI evolution. The mathematical literature contains various generative models of random graphs. It is a major, still largely open question, which of these models (if any) can properly reproduce various biologically interesting networks. Here,  consider this problem where the graph at hand is the PPI network of Saccharomyces cerevisiae.  are trying to distinguishing beten a model family which performs a process of copying neighbors, represented by the duplication–divergence (DD) model, and models which do not copy neighbors, with the Barabasi–Albert (BA) preferential attachment model as a leading example.#R##N##R##N#Results: The observed property of the network is the distribution of maximal bicliques in the graph. This is a novel criterion to distinguish beten models in this area. It is particularly appropriate for this purpose, since it reflects the graph's growth pattern under either model. This test clearly favors the DD model. In particular, for the BA model, the vast majority (92.9%) of the bicliques with both sides ≥4 must be already embedded in the model's seed graph, whereas the corresponding figure for the DD model is only 5.1%. Our results, based on the biclique perspective, conclusively show that a naive unmodified DD model can capture a key aspect of PPI networks.#R##N##R##N#Contact: li.ca.ijuh.sc@10sveger; li.ca.ijuh.cc@llahcim; li.ca.ijuh.sc@itan#R##N##R##N#Supplementary information: Supplementary data are available at Bioinformatics online.
['Regev Schweiger', 'Michal Linial', 'Nathan Linial']
I am writing a research paper on a idea Generative probabilistic models for protein–protein interaction networks—the biclique perspective
[{"from": "human", "value": "I am writing a research paper on a idea Generative probabilistic models for protein\u2013protein interaction networks\u2014the biclique perspective"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Motivation: Much of the large-scale molecular data from living cells can be represented in terms of networks. Such networks occupy a central position in cellular systems biology. In the protein\u2013protein interaction (PPI) network, nodes represent proteins and edges represent connections beten them, based on experimental evidence. As PPI networks are rich and complex, a mathematical model is sought to capture their properties and shed light on PPI evolution. The mathematical literature contains various generative models of random graphs. It is a major, still largely open question, which of these models (if any) can properly reproduce various biologically interesting networks. Here,  consider this problem where the graph at hand is the PPI network of Saccharomyces cerevisiae.  are trying to distinguishing beten a model family which performs a process of copying neighbors, represented by the duplication\u2013divergence (DD) model, and models which do not copy neighbors, with the Barabasi\u2013Albert (BA) preferential attachment model as a leading example.#R##N##R##N#Results: The observed property of the network is the distribution of maximal bicliques in the graph. This is a novel criterion to distinguish beten models in this area. It is particularly appropriate for this purpose, since it reflects the graph's growth pattern under either model. This test clearly favors the DD model. In particular, for the BA model, the vast majority (92.9%) of the bicliques with both sides \u22654 must be already embedded in the model's seed graph, whereas the corresponding figure for the DD model is only 5.1%. Our results, based on the biclique perspective, conclusively show that a naive unmodified DD model can capture a key aspect of PPI networks.#R##N##R##N#Contact: li.ca.ijuh.sc@10sveger; li.ca.ijuh.cc@llahcim; li.ca.ijuh.sc@itan#R##N##R##N#Supplementary information: Supplementary data are available at Bioinformatics online."}]