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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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The potency of a drug is the measure of its ability to produce a biological response and can be compared by looking at the half-maximum effective concentration or EC50 values of different drugs. A lower EC50 value indicates higher potency of the drug. In the dose–response curve of two antihypertensive drugs, candesartan and irbesartan, a significant difference is observed in their EC50 values. A lower EC50 value for candesartan indicates that it is more potent than irbesartan, as it...
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Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation
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A hierarchical constrained density regression model for predicting cluster-level dose-response.

Michael L Pennell1, Matthew W Wheeler2, Scott S Auerbach3

  • 1Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, Ohio, USA.

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Summary
This summary is machine-generated.

New statistical methods are needed for chemical toxicity screening. Constrained Logistic Density Regression (COLDER) models gene expression data simultaneously, improving analysis of transcriptomic assays for chemical safety assessment.

Keywords:
Bayesian nonparametricsbenchmark dosedose-response modelingfunctional data analysisstick-breaking processtoxicogenomics

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Area of Science:

  • Toxicology
  • Bioinformatics
  • Statistical Modeling

Background:

  • Transcriptomic assays generate large datasets for chemical toxicity screening.
  • Current methods analyze genes individually and lack flexibility for high exposure levels.
  • Existing approaches do not share information among genes within biological pathways.

Purpose of the Study:

  • To introduce Constrained Logistic Density Regression (COLDER) for simultaneous modeling of gene expression data.
  • To address limitations of current statistical methods in toxicity screening.
  • To develop a method that accounts for dose-response shape changes and shares pathway information.

Main Methods:

  • Proposed Constrained Logistic Density Regression (COLDER) model.
  • Utilized a discrete logistic stick-breaking process (LSBP) for prior assignment.
  • Incorporated gene-level characteristics (e.g., pathway membership) and biologically plausible shape constraints.
  • Estimated posterior distribution for benchmark dose within gene pathways.

Main Results:

  • COLDER enables simultaneous modeling of expression data across multiple genes.
  • The method allows information sharing among genes within the same pathways.
  • Posterior distribution for benchmark dose can be directly estimated.
  • Model performance evaluated through simulation and a National Toxicology Program study.

Conclusions:

  • COLDER offers an improved statistical approach for analyzing high-throughput toxicity data.
  • The method enhances the synthesis of large transcriptomic datasets for chemical safety.
  • COLDER provides a more biologically plausible and informative analysis of dose-response relationships.