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As a consequence, Monte Carlo in multiple testing settings is rapidly becoming important (Sandve et al., 2010).
In the same way, although the ideas here presented on MC in multiple testing settings are general, we believe their relevance will increase strongly along with the future developments in e.g. next-generation sequencing, making them an important part of a bioinformaticians toolbox in the future.
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The AGTC questionnaire has been tested in multiple settings and in Brazil as well.
Other directions include developing an optimal way to make corrections for multiple testing in settings where there are potentially more covariates (SNPs) than observations.
Additionally, current validation methods may unnecessarily inflate the rate of false negative conclusions by requiring strict multiple testing adjustments in settings where false positive conclusions could be effectively minimized with additional confirmatory data [ 8].
In our analysis, we used default settings with Bonferroni correction for multiple testing.
Despite their intuitive appeal for reducing the dimensionality of large-scale exposure data, the use of multiple testing methods has been frequently criticized in epidemiologic settings.
It features flexible experimental settings, quality control, automatic filtering, normalization, multiple testing, and differential analyses on arbitrary genomic regions.
In order to evaluate all possible associations between clinical measurements and the presence of multiple Plasmodium infections in three transmission settings it has been necessary to use multiple testing.
The sequence files were subsequently submitted to evaluation by SigniSite with the following settings: Significance threshold = 0.05, Correction for multiple testing using the Bonferroni single-step, Consider values given in fasta header and Choose decreasing order.
This means that identifying the appropriate method along with parameter settings for a particular dataset is challenging and requires multiple testing.
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