Sentence examples for bias from the data from inspiring English sources

As dPCR does not need standard curves for quantification, this approach removes this potential source of bias from the data.

The purpose of normalization is to remove as much non-biological variation, "noise" and bias, from the data as possible and to make it possible to compare results within or between experiments.

Contrarily, the proposed use of specifically hybridized probes largely removes this bias from the data and provides a reliable measure of the degradation degree which can be consistently compared between different arrays.

Loess normalisation subtracts a Loess regression curve from the MA-transformed data in order to remove dye bias from the data, while scale normalisation between arrays ensures that signal intensities are comparable across arrays.

Though Huang and colleagues have presented the first attempt at computationally modeling protein stability, this work can be improved through the removal of potential experimental bias from the data, and the use of a computational method that can explain its predictions.

As the use of variance stabilizing normalisation (VSN) [ 51] alone was unable to completely remove colour biases from the data (data not shown), the data was first normalised within arrays with lowess using the package Limma (Linear models for microarrays) [ 19].

In order to determine the degree of bias of the compounds 1 and 12, we calculated their bias factors from the data sets in Figure 2 and Figure 3A,B as well as binding affinity values (Supplementary Figure S2) using the operational model.

To assess possible sample bias from the limited data of A1C, we conducted a sensitivity analysis restricted to data from 1999 2006 and arrived at the same conclusions as for the full dataset (1999 2008); therefore, we presented our findings based on the full 1999 2008 dataset.

Sophisticated variation of the attack in Fig. 1 includes (1) bias-injection cyber-physical attacks, in which the new data injected by the adversary corresponds to a bias from the legitimate data, with the aim of leading the system to wrong control decisions (e.g., to cause malfunction in the long-term); and (2) geometric-injection cyber-physical attacks, in which the bias is gradually injected.

The bias calculated from the data corresponds to 0.2, which is much smaller than 2.1.

One possible reason for the slower decrease of the original BYEC classifier may be that more subclassifiers balance the bias from the original data set.