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Related Concept Videos

Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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 squares (OLS)...
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
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Pharmacodynamic Models: Linear Concentration–Effect Model01:15

Pharmacodynamic Models: Linear Concentration–Effect Model

The linear concentration–effect model, underpinned by the principle that pharmacological effect (E) is directly proportional to plasma drug concentration (C), emerges as a pivotal simplification of the Emax model for conditions where C is significantly less than EC50. This model portrays a linear trajectory of the concentration–effect relationship when drug levels are markedly below the EC50 threshold.Despite its inherent assumption of continuous effect augmentation with increasing drug...
Column Efficiency: Rate Theory01:12

Column Efficiency: Rate Theory

The rate theory of chromatography provides quantitative insight into the shapes and widths of elution bands. These bands are based on the random-walk mechanism governing molecular migration within a column. The Gaussian profile of chromatographic bands arises from the cumulative effect of random molecular motions as they progress through the column.
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Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
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Quantification of Hydrogen Concentrations in Surface and Interface Layers and Bulk Materials through Depth Profiling with Nuclear Reaction Analysis
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Published on: March 29, 2016

Bi-Linear Regression for O Quantification: Modeling across the Elution Profile.

Jeanette E Eckel-Passow1, Douglas W Mahoney, Ann L Oberg

  • 1Division of Biomedical Statistics and Informatics.

Journal of Proteomics & Bioinformatics
|August 27, 2011
PubMed
Summary

Bi-linear regression offers a unified model for quantifying labeled mass-spectrometry data. This method enhances precision and accuracy in proteomic profiling by analyzing the entire elution profile.

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Lipidomics and Transcriptomics in Neurological Diseases
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Area of Science:

  • Proteomics
  • Analytical Chemistry
  • Biostatistics

Background:

  • Interpreting labeled mass-spectrometry data for large-scale proteomic profiling is complex.
  • Automated algorithms are crucial for accurate quantification and analysis.
  • Existing methods may not fully leverage elution profile information.

Purpose of the Study:

  • To propose and validate a bi-linear regression model for quantifying relative peptide abundance in labeled mass-spectrometry data.
  • To provide a unified model that accounts for the entire elution profile.
  • To enable simultaneous comparison of two samples using (18)O stable-isotope labeling.

Main Methods:

  • Application of bi-linear regression models to (18)O stable-isotope labeled mass-spectrometry data.
  • Modeling peptide abundance across the elution profile, assuming multiplicative differences in overall abundance and constant relative abundance between samples.
  • Estimation of isotope incorporation rate as a data quality measure.

Main Results:

  • Bi-linear regression provides more precise and accurate abundance estimates compared to spectrum-independent methods.
  • Precision of abundance estimates is increased by one to two orders of magnitude.
  • The model effectively utilizes information across the entire elution profile for quantification.

Conclusions:

  • Bi-linear regression is a powerful, unified approach for quantifying labeled mass-spectrometry data in large-scale proteomic profiling.
  • The method offers significant improvements in precision and accuracy for relative abundance estimation.
  • The model's ability to estimate isotope incorporation provides a valuable data quality metric.