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In this paper, we demonstrate that segregation analysis is an old solution to a new problem, by applying a segregation analysis method to simulated data to explore the inference of copy number alleles at the Agouti locus in sheep.
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Previous work has systematically explored the inference of triple quadrupole MS/MS fragmentation from ion trap MS/MS data and from HCD data.
Xue [5] used the validation data to explore the empirical likelihood inference of nonlinear semiparasitic error-in-variable models.
Therefore, sensitivity analyses, where we explore the robustness of inferences as assumptions change, are important.
Standard implementation of MI assumes the data are MAR, so it is widely recommended to perform a sensitivity analysis to explore the robustness of inferences to departures from the MAR assumption.
To explore the sensitivity of the inferences to departures from the MAR assumption, we applied the method proposed by Carpenter et al. (2007).
We explore the role of lightweight inference techniques in creating highly automated engineering support environments for the development of computer-based systems.
DOI: http://dx.doi.org/10.7554/eLife.03753.015 To explore the robustness of this inference, we compared the predicted dynamics of the model to an additional type of data: the fraction of positive-sense RNA molecules translating at each time point.
In the application to real data, we explore the performance of GRN inferences and eQTL identifications through the inferred networks.
The decision tree is designed to drive the inference engine to explore the route of selection procedure of modeling.
In such instances, conducting sensitivity analyses can be a viable way to allow us not to impose the nondifferential misclassification mechanism but enable us to explore the impact of misclassification on inference results.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com