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Conversely, although metabolic model predictions have previously been shown to accurately predict the effects of many targeted gene knockouts [ 10] and have been applied to select individual and multiple antibacterial targets [ 21, 22], these metabolic models have not yet been capable of pairing these targets with compounds.
For the training sample, prediction of class membership was based on the internal voting scheme for RF, whereas for PLS-DA a softmax method was used, whereby the predicted class, c∗, is the largest class probability after model predictions have been transformed on a [0,1] interval (with unit sum), that is (5) Y = y c where c ∗ = arg max 0 ≤ Z c ≤ 1, ∑ Z c = 1 (Z ^ c ).
The model predictions have been verified through some preliminary experiments.
The model predictions have been compared with the experimental measurements to validate the model.
The model predictions have been verified by comparing them with the published experimental data.
Model predictions have been validated against experimental measurements available for three different foam materials.
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The comparison between the experimental output of interconnected circuits and mathematical model predictions has been useful to evaluate the predictability boundaries of biological components when re-used to engineer diverse systems [ 20].
An excellent agreement between experimental results and the model predictions has been obtained.
A good matching between experimental results and model predictions has been obtained for all the investigated conditions.
Hence, a data set specific choice of modeling algorithm and perhaps also the usage of combined model predictions, has the potential of increasing the model applicability beyond what is achievable with a single algorithm [3].
Our expanded model predictions had relative RMS errors within this range (9-21%).
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