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We employ a Bayesian approach involving importance sampling of the posterior predictive distribution to predict the efficacy of a new measurement at reducing the uncertainty of a selected prediction.
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For each miRNA considered, an enrichment of its predicted targets according to one selected prediction tool is defined for the 'Down' and the 'Up' gene sets, significance being evaluated using the hypergeometric law.
The results of the selected prediction tools are formatted and displayed in a single table, facilitating the comparison of the different predictions.
The finally selected prediction model should assign to each (new) subject a probabilistic prediction for the potential values of the response variable based on the subjects predictor values.
The performance of selected prediction methods have been validated against the experimental results.
The experimental creep coefficient data have been compared with the estimated values using selected prediction models.
We finally tested the selected prediction models on the internal and external validation groups using ROC analysis.
We have developed and implemented an energy-based computational approach to miRNA target prediction, applied it to a standard set of known human mRNA sequences at whole-transcriptome scale, and experimentally measured the change in mRNA level for a small number of selected predictions.
Even if the adopted procedure was not sensitive enough to recover all known miRNA families in the IASMA genome, among the selected predictions a significant number of homologues to the At and Genoscope Pinot noir genomes was recovered, and precursors sharing their sequence with putative priMIRs that were cloned to ESTs were also retrieved.
A summary is given of selected predictions and their comparisons with experiments performed at the Railway Technical Research Institute in Tokyo at train Mach numbers as large as 0.35 (∼425km/h).
Finally, selected predictions from this technique match very well with a multi-term harmonic balance method and with direct numerical integration, wherever applicable.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com