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Coalescent theory is a sample-based approach and is concerned with making inferences about the genetics of populations under a statistical framework [15].
Finally, expected read counts on HLA alleles are estimated by variational Bayesian inference under a statistical framework for HLA typing.
Then, HLA-VBSeq optimizes both read alignments to the HLA allele sequences and relative quantities of reads on HLA alleles simultaneously under a statistical framework by variational Bayesian inference.
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We therefore set out to develop both a novel approach for high-throughput cell-based screening of multiple compound concentrations and a statistical framework to define synergy as a probabilistic lifetime process under single and combined chemical perturbations.
While we conclude that we are still far from having a satisfactory null model for PPI networks, we provide a statistical framework for assessing the fit of potential new models under the aspect of similarity of small subgraph counts.
First, it is based on a statistical framework.
Moreover, a probabilistic model provides a statistical framework for inference.
A statistical framework is used to explore this trade-off.
Within a statistical framework, Tyralis and Koutsoyiannis (2014, 2017) use Bayesian techniques for probabilistic climate forecasts under the established assumption of long-range dependence of the observed time series.
This paper proposes a statistical framework to convert a full-body motion to multiple descriptive sentences.
Siuly and Li [1] proposed a statistical framework for multiclass EEG signal classifications.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com