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Related Concept Videos

Prediction Intervals01:03

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
The...
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting the...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

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Related Experiment Video

Updated: Jun 26, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Parallelized prediction error estimation for evaluation of high-dimensional models.

Christine Porzelius1, Harald Binder, Martin Schumacher

  • 1Institute of Medical Biometry and Medical Informatics, University Medical Center Freiburg, 79104 Freiburg, Germany. cp@fdm.uni-freiburg.de

Bioinformatics (Oxford, England)
|January 30, 2009
PubMed
Summary
This summary is machine-generated.

The R package peperr simplifies reliable prediction error estimation using resampling techniques in bioinformatics. It addresses implementation challenges and computational demands, aiding model validation and performance evaluation.

Related Experiment Videos

Last Updated: Jun 26, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Statistical Modeling

Background:

  • Accurate model validation is crucial for bioinformatics applications.
  • Resampling techniques are essential for validating predictive models.
  • Implementation complexity and computational cost hinder the use of resampling.

Purpose of the Study:

  • To introduce the R package peperr for reliable prediction error estimation.
  • To facilitate the use of resampling techniques in bioinformatics.
  • To accelerate resampling through parallel computation.

Main Methods:

  • Development of the R package peperr.
  • Implementation of resampling techniques for prediction error estimation.
  • Integration of parallel execution for computational acceleration.
  • Inclusion of diagnostic plots for performance evaluation.

Main Results:

  • peperr provides an accessible interface for resampling-based model validation.
  • The package supports parallel execution on compute clusters.
  • Diagnostic plots aid in identifying issues with high-dimensional data.

Conclusions:

  • peperr simplifies and enhances the validation of predictive models in bioinformatics.
  • The package promotes reliable prediction error estimation.
  • It aids researchers in evaluating model performance, especially with complex datasets.