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Replicated response datasets are compressed utilizing the signal to noise ratio and then they are homogenized by simple rank-ordering.
In this paper we present a comparative analysis of these two approaches using the principal component analysis in combination with raw as well as standardized data, as a common method of polymer selection utilizing two SAW chemical sensor array response datasets available from the published literature.
Thus, the pathway level transcript CV estimate identifies predicted metabolic pathway responses within the response datasets.
Interestingly, both studies querying genetic variation showed similar levels of transcript variance, albeit lower levels than calculated for the response datasets (Tables 1 and 2).
To test the utility of pathway CV estimates for interpreting metabolic pathway response to experimental variation, I investigated the response datasets.
We analyzed both Toll and non-Toll ligand response datasets.
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Then, their collected response dataset itself was used to demonstrate the iSAT tool.
Besides the historical and performance datasets, a response dataset was available, indicating whether a consumer accepted the offer or not.
In their paper, the authors apply this algorithm to a yeast stress response dataset.
The treatment response dataset contained several probes for some factors, which were evaluated individually to identify technical or biological differences.
Recording lengths were 65 ± 2.4 s (SEM) for the intraoperative dataset and 286 ± 15 s (SEM) for the levodopa response dataset.
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