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    Multi-model and network inference based on ensemble estimates: avoiding the madness of crowds

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    Author
    Stumpf, MPH
    Date
    2020-10-28
    Source Title
    Journal of the Royal Society Interface
    Publisher
    ROYAL SOC
    University of Melbourne Author/s
    Stumpf, Michael
    Affiliation
    School of BioSciences
    Metadata
    Show full item record
    Document Type
    Journal Article
    Citations
    Stumpf, M. P. H. (2020). Multi-model and network inference based on ensemble estimates: avoiding the madness of crowds. JOURNAL OF THE ROYAL SOCIETY INTERFACE, 17 (171), https://doi.org/10.1098/rsif.2020.0419.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/252944
    DOI
    10.1098/rsif.2020.0419
    Abstract
    Recent progress in theoretical systems biology, applied mathematics and computational statistics allows us to compare the performance of different candidate models at describing a particular biological system quantitatively. Model selection has been applied with great success to problems where a small number-typically less than 10-of models are compared, but recent studies have started to consider thousands and even millions of candidate models. Often, however, we are left with sets of models that are compatible with the data, and then we can use ensembles of models to make predictions. These ensembles can have very desirable characteristics, but as I show here are not guaranteed to improve on individual estimators or predictors. I will show in the cases of model selection and network inference when we can trust ensembles, and when we should be cautious. The analyses suggest that the careful construction of an ensemble-choosing good predictors-is of paramount importance, more than had perhaps been realized before: merely adding different methods does not suffice. The success of ensemble network inference methods is also shown to rest on their ability to suppress false-positive results. A Jupyter notebook which allows carrying out an assessment of ensemble estimators is provided.

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