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Uncertainty Estimation for Quantitative Agarose Gel Electrophoresis of Nucleic Acids.

Konstantin SemenovAleksandr TaraskinAlexandra YurchenkoIrina L BaranovskayaLada V PurvinshNatalia GyulikhandanovaAndrey V Vasin
Published in: Sensors (Basel, Switzerland) (2023)
This paper considers the evaluation of uncertainty of quantitative gel electrophoresis. To date, such uncertainty estimation presented in the literature are based on the multiple measurements performed for assessing the intra- and interlaboratory reproducibility using standard samples. This paper shows how to estimate the uncertainty in cases where we cannot study scattering components of the results. The first point is dedicated to a case where we have standard samples (the direct expressions are shown). The second point considers the situation when standard samples are absent (the algorithm for estimating the lower bound for uncertainty is discussed). The role of the data processing algorithm is demonstrated.
Keyphrases
  • machine learning
  • deep learning
  • systematic review
  • high resolution
  • electronic health record
  • mass spectrometry