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Michael P. H. Stumpf

Researcher at University of Melbourne

Publications -  275
Citations -  15492

Michael P. H. Stumpf is an academic researcher from University of Melbourne. The author has contributed to research in topics: Population & Systems biology. The author has an hindex of 57, co-authored 264 publications receiving 13610 citations. Previous affiliations of Michael P. H. Stumpf include Imperial College London & University of Oxford.

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Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems

TL;DR: This paper discusses and applies an ABC method based on sequential Monte Carlo (SMC) to estimate parameters of dynamical models and develops ABC SMC as a tool for model selection; given a range of different mathematical descriptions, it is able to choose the best model using the standard Bayesian model selection apparatus.
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Evolution of pathogenicity and sexual reproduction in eight Candida genomes.

TL;DR: There are significant expansions of cell wall, secreted and transporter gene families in pathogenic species, suggesting adaptations associated with virulence in Candida albicans species.
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Estimating the size of the human interactome

TL;DR: It is found that the human interaction network is one order of magnitude bigger than the Drosophila melanogaster interactome and ≈3 times bigger than in Caenorhabditis elegans.
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Critical Truths About Power Laws

TL;DR: Although power laws have been reported in areas ranging from finance and molecular biology to geophysics and the Internet, the data are typically insufficient and the mechanistic insights are almost always too limited for the identification of power-law behavior to be scientifically useful.
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Subnets of scale-free networks are not scale-free: sampling properties of networks.

TL;DR: The sampling properties of a network's degree distribution under the most parsimonious sampling scheme is discussed and it is shown that this condition is indeed satisfied for some important classes of networks, notably classical random graphs and exponential random graphs.