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Related Concept Videos

Genetic Screens02:46

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...

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High-throughput Screening for Chemical Modulators of Post-transcriptionally Regulated Genes
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Exploiting domain knowledge for improved quantitative high-throughput screening curve fitting.

Charles Bergeron1, Gregory Moore, Michael Krein

  • 1Department of Mathematical Sciences, Rensselaer Polytechnic Institute, 110 8th Street, Troy, New York 12180, USA. chbergeron@gmail.com

Journal of Chemical Information and Modeling
|October 18, 2011
PubMed
Summary
This summary is machine-generated.

Domain knowledge fitter (DK-fitter) improves Hill equation fitting in quantitative high-throughput screening (qHTS) assays. This method enhances potency evaluation by providing more reliable data analysis from screening experiments.

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Area of Science:

  • Biochemistry
  • Pharmacology
  • Data Science

Background:

  • Quantitative high-throughput screening (qHTS) assays are crucial for drug discovery.
  • Standard Hill equation fitting in qHTS often yields poor results.
  • Accurate parameter estimation is vital for reliable potency evaluation.

Purpose of the Study:

  • To introduce and validate a novel nonlinear regression method, the domain knowledge fitter (DK-fitter).
  • To improve the accuracy of Hill equation fitting in qHTS data.
  • To enhance the extraction of high-quality information from screening assays.

Main Methods:

  • Development of the DK-fitter algorithm, incorporating prior knowledge into nonlinear regression.
  • Application of DK-fitter to 44 public qHTS datasets.
  • Validation of DK-fitter using three unbiased approaches, including simulated data generation.

Main Results:

  • DK-fitter demonstrates improved performance in fitting the Hill equation compared to standard methods.
  • The method successfully extracts higher quality information from qHTS data.
  • Validation confirms the robustness and reliability of the DK-fitter approach.

Conclusions:

  • DK-fitter offers a significant advancement in analyzing qHTS data.
  • This technique leads to more accurate potency evaluations.
  • The study promotes better utilization of screening data for drug discovery and development.