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This was done by evaluating our data and literature to make the best and most objective choice possible.
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Straight lines were obtained when we evaluated our data by applying Langmuir and Freundlich isotherm equations (Figs. 3, 4a e) which suggested that our data is correlated with both the isotherm models.
We note that a similar metric has previously been used to evaluate the efficiency of TargetScan scores [ 8, 18], and that this set of miRNA over-expression assays were not used in BayMiR to obtain the scores; thus, we are not influencing the results of our evaluation by either selecting bias metrics or by evaluating our model on the training data.
We describe the degree of statistical identifiability in our system by evaluating a data set X A, generated with a set of true parameters Θ0, and computing the probability that X A could have been generated with sets of false parameters { Θ j } j = 1, …, 100.
In our study we seek to determine whether reconstruction accuracy is improved by evaluating the data using only the relevant invariants.
We investigated the effectiveness of our intervention by evaluating pre post survey data.
This is analyzed by evaluating sensor data during casting.
It was performed by evaluating the data from the vital signs registered on the Ward in the 24 hours prior IHCA.
This objective was addressed by evaluating DLT data and other safety data.
First, the iron oxide core size was determined by evaluating SAXS data and comparing the obtained hard sphere size to TEM data.
In addition, the parameters employed in filtering and merging steps of iSVP are determined by evaluating simulation data for a wide range of sizes.
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