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Under some conditions on the prior and design matrix, we identify the analytic form of the Bayes factor and show that the Bayes factor is consistent, i.e. converges to infinity in probability under the parametric null model, while converges to zero under the semiparametric alternative, as the sample size increases.
Within this matrix, we identify the scenarios in which the combination of imatinib with a stem cell-activating drug such as IFN α appears beneficial for the clinical outcome and the reduction of the minimal residual disease.
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To implement this Bayesian matrix we identified these 16 residues through MSAs made with MUSCLE version 3.6 [39], and structural models that were created with the SWISS-MODEL workspace (E-value limit 1.0E-6) and refined and visualized with Swiss-PdbViewer version 3.7 [3.7.
By simple inspection of the CSV matrix we identified two outlier studies that yielded prediction models no better than random guessing in new studies.
In a study covering a large number of matrices, we identify the ISAI preconditioner as an attractive alternative to exact triangular solves in the context of incomplete factorization preconditioning.
Separate matrices were developed for each stakeholder category- patients, providers, and staff, and, from these matrices, we identified prominent themes among stakeholder groups.
We repeated the process for (B \ A). 2) Using these similarity matrices we identified the nearest neighbor for each gene: the gene with the highest similarity score.
Using the MatInspector program, which uses TRANSFAC transcription factor binding site matrices, we identified a number of putative transcription factor binding sites upstream of the Jab1 transcription start site.
Using Drosophila-specific position-weight matrices, we identified promoters containing TATA box, Initiator, Downstream Promoter Element (DPE), and Motif Ten Element (MTE), as well as core elements discovered in Human (TFIIB Recognition Element BREE) and Downstream Core Element (DCE)).
From the rank matrix we can identify the best alternative match.
Using non-negative matrix factorization, we identified mutational signatures that did not segregate by genetic background or histology.
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com