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Both methods employ an analysis-masking-synthesis approach.
All methods employ an inverted chamber covering a small area of soil.
Further, except for the method of Kumazawa and Ogata (2014), none of the above cited methods employ an explicit roughness penalty function to constrain the wiggliness of the estimated (mu (t)), and therefore, could lead to estimates that overfit the data.
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Most known separation methods employ a column chromatography technique [19].
The method employed by RN corresponds to the first step of C3NET but also of ARACNE, however, both methods employ a second step.
These methods employ a range of analytical techniques that directly affect both the sensitivity and selectivity of the analysis.
Current network inference methods employ a number of different strategies to reduce the search space of network structure solutions.
Filter methods employ a criterion to evaluate each feature individually and is independent of the model [ 10].
However, all these methods employ a single type of phenotypic information (i.e. transcription data), whereas post-transcriptional regulation has a recognized and substantial effect on a phenotype.
Most methods employ a statistical approach to identify interacting marker pairs based on deviation from a null distribution and estimation of type I error.
These methods employ a variety of techniques and data, including evolutionary information, structural templates and protein interaction network information, to predict whether sets of proteins interact [ 14].
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