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Our approach was developed for tissue-specific gene predictions, but its application to tissue-selective gene predictions is straightforward.
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Our predictions were straightforward.
As in Experiment 1, the predictions were straightforward: If a functional division in hemispheric processing exists right up to fixation, performance should be superior for words displayed to the right of fixation in extrafoveal and foveal locations.
Once these models are constructed, the classification/prediction as well as the combination (e.g., ensemble scheme) of predictions are straightforward.
In plants, these predictions are straightforward since a plant gene typically contains a sequence that is complementary to the sequence of the whole miRNA [ 20].
In this first case, i.e. when gettering dominates, the prediction is straightforward and the critical mole fraction can be calculated from the pumping speed, the current, the sputter yield of the target material and an average value for sticking coefficient of the reactive gas on the deposited target material.
First we removed two outlier cases for passengers whose future tier prediction is straightforward or has little merit: 1) Passengers who fly less than one return trip in six years.
Target prediction for miRNAs is straightforward because it is assumed that most of them match their targets with almost perfect complementarity [ 9, 47].
The logic behind these dire predictions is pretty straightforward.
Since the multi-state model under consideration contains cluster-specific random effects, the definition of the predictions is not straightforward.
The prediction for E. coli is straightforward as the pathway only involves one operon, and our predictions, assigning all within-operon PIs, are verified [ 35] (Additional file 11).
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CEO of Professional Science Editing for Scientists @ prosciediting.com