dorsal/arxiv
View SchemaStatistical model selection methods applied to biological networks
| Authors | M. P. H. Stumpf, P. J. Ingram, I. Nouvel, C. Wiuf |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0506013 |
| URL | https://arxiv.org/abs/q-bio/0506013 |
Abstract
Many biological networks have been labelled scale-free as their degree distribution can be approximately described by a powerlaw distribution. While the degree distribution does not summarize all aspects of a network it has often been suggested that its functional form contains important clues as to underlying evolutionary processes that have shaped the network. Generally determining the appropriate functional form for the degree distribution has been fitted in an ad-hoc fashion. Here we apply formal statistical model selection methods to determine which functional form best describes degree distributions of protein interaction and metabolic networks. We interpret the degree distribution as belonging to a class of probability models and determine which of these models provides the best description for the empirical data using maximum likelihood inference, composite likelihood methods, the Akaike information criterion and goodness-of-fit tests. The whole data is used in order to determine the parameter that best explains the data under a given model (e.g. scale-free or random graph). As we will show, present protein interaction and metabolic network data from different organisms suggests that simple scale-free models do not provide an adequate description of real network data.
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"abstract": "Many biological networks have been labelled scale-free as their degree\ndistribution can be approximately described by a powerlaw distribution. While\nthe degree distribution does not summarize all aspects of a network it has\noften been suggested that its functional form contains important clues as to\nunderlying evolutionary processes that have shaped the network. Generally\ndetermining the appropriate functional form for the degree distribution has\nbeen fitted in an ad-hoc fashion.\n Here we apply formal statistical model selection methods to determine which\nfunctional form best describes degree distributions of protein interaction and\nmetabolic networks. We interpret the degree distribution as belonging to a\nclass of probability models and determine which of these models provides the\nbest description for the empirical data using maximum likelihood inference,\ncomposite likelihood methods, the Akaike information criterion and\ngoodness-of-fit tests. The whole data is used in order to determine the\nparameter that best explains the data under a given model (e.g. scale-free or\nrandom graph). As we will show, present protein interaction and metabolic\nnetwork data from different organisms suggests that simple scale-free models do\nnot provide an adequate description of real network data.",
"arxiv_id": "q-bio/0506013",
"authors": [
"M. P. H. Stumpf",
"P. J. Ingram",
"I. Nouvel",
"C. Wiuf"
],
"categories": [
"q-bio.MN",
"q-bio.OT"
],
"title": "Statistical model selection methods applied to biological networks",
"url": "https://arxiv.org/abs/q-bio/0506013"
},
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