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Once a gold standard has been prepared for a particular application, the performance of a method can be evaluated using numerous factors (Fig. 1h).
As all these parameters can have significant effects on the resulting modules (and thus the performance of a method), we assessed how well automatic parameter estimation can improve method performance.
The performance of a method for ionic conductivity measurements in physical gels with use of thermal scanning approach is investigated.
To address both problems, we optimized the parameters for every method with a grid search (Supplementary Note 2) and used an approach akin to cross-validation where the optimal parameter settings from one dataset were used to assess the performance of a method on another dataset (Fig. 1c).
The performance of a method duly optimized for the extraction and simultaneous detection of soy, egg and milk allergens in a cookie food matrix by microHPLC ESI-MS/MS, is herein reported.
It is shown not only that the performance of a method depends on the given source distribution, but also that the methods differ in terms of their sensitivity to imperfect input data.
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Detailed review: This manuscript investigates the performance of C3NET, a method developed to infer the structure of gene regulatory networks (GRNs) from gene expression data.
Another standard method for evaluating the performance of a prioritization method is to consider the precision recall curve (Vanunu et al., 2010).
The performance of such a method when using magnitude-reconstructed images is highly sensitive to the inversion recovery time selected.
As Ambroise and McLachlan [ 61] point out, the performance of a classification method may be overestimated when using the Leave-out-out method.
We finally calculated the harmonic mean between the normalized versions of all four scores to obtain a final score representing the performance of a particular method on a given dataset and module definition.
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