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As different operon predictions may differ in a number of cases (Brouwer et al. 2008), we also calculated all results using the operons predicted in the Database of prOkaryotic OpeRons (DOOR) at http://csbl1.bmb.uga.edu/OperonDB (last accessed November 20, 2013; Mao et al. 2009; supplementary text S1, Supplementary Material online).

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It turns out, however, that in these circumstances the prediction may differ depending on the chosen formulation.

However, in practice with finite sample data, the true model is not known, and the models most suitable for estimation and prediction may differ.

Since clients' emotional context at time of prediction may differ considerably from one experienced at time of the future event, decisions around end of life treatment options may change over time.

Furthermore, we validated our prediction model externally and, finally, risks and predictors may differ between American and European populations and healthcare systems.

This underlines that the concepts of accuracy and utility of a prediction model may differ drastically in practice.

A simple but important example is that the information of cancer subtype must be considered before model development and use to ensure the reliability of any prediction, since the prognosis may differ significantly between subtypes [34].

A younger age at diagnosis is more likely to reflect an underlying genetic susceptibility to the disease, thus the contribution of MC1R and traditional factors to risk prediction in our study may differ for those diagnosed at older ages or for other ethnicities.

In that case, predictions about affectedness in relatives may differ for parents and siblings.

Despite predictions that TE population dynamics may differ markedly between selfing and outcrossing species, comparative data are quite rare.

Model predictions using expected host longevity may differ from those obtained using host mortality rate [ 78], yet in our one-generation study these effects are most likely negligible.

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