Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

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

1.4K
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...
1.4K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

9.7K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
9.7K
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

4.9K
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...
4.9K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.6K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
5.6K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

387
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...
387
Application of Linearization and Approximation01:29

Application of Linearization and Approximation

129
A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
129

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

SNP-based prediction of schizophrenia using machine learning.

Bioinformatics advances·2026
Same author

Predicting Intensive Care Unit Admission in COVID-19-Infected Pregnant Women Using Machine Learning.

Journal of clinical medicine·2025
Same author

Diagnosis of Endometriosis Based on Comorbidities: A Machine Learning Approach.

Biomedicines·2023
Same author

Prevalence of HIV in Kazakhstan 2010-2020 and Its Forecasting for the Next 10 Years.

HIV/AIDS (Auckland, N.Z.)·2023
Same author

Predicting 1-year mortality of patients with diabetes mellitus in Kazakhstan based on administrative health data using machine learning.

Scientific reports·2023
Same author

An Ensemble CNN for Subject-Independent Classification of Motor Imagery-based EEG.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2021

Related Experiment Video

Updated: Mar 16, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.8K

An efficient method to estimate the optimum regularization parameter in RLDA.

Daniyar Bakir1, Alex Pappachen James1, Amin Zollanvari1

  • 1Department of Electrical and Electronics Engineering, Nazarbayev University, Astana, 010000, Kazakhstan.

Bioinformatics (Oxford, England)
|August 4, 2016
PubMed
Summary

This study introduces an efficient range-search technique to optimize regularization parameters for regularized linear discriminant analysis (RLDA). The method significantly improves accuracy and computational speed compared to existing techniques for high-dimensional genomic data analysis.

More Related Videos

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.9K

Related Experiment Videos

Last Updated: Mar 16, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.8K
Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.9K

Area of Science:

  • Genomics
  • Statistical Learning
  • Bioinformatics

Background:

  • High-throughput genomic data analysis presents challenges for statistical learning, particularly when the number of variables exceeds the sample size.
  • Classical methods like linear discriminant analysis (LDA) perform poorly due to ill-conditioned covariance matrices in high-dimensional settings.
  • Regularized LDA (RLDA) offers an improvement but its performance is sensitive to the regularization parameter choice.

Purpose of the Study:

  • To develop an efficient range-search technique for estimating the optimal regularization parameter in RLDA.
  • To assess the robustness and performance of the proposed technique against existing methods.
  • To provide a faster and more accurate approach for biomarker discovery in high-dimensional genomic data.

Main Methods:

  • A novel range-search technique is proposed for efficient estimation of the optimum regularization parameter in RLDA.
  • Extensive simulations using synthetic and gene expression microarray data were conducted.
  • The proposed method's performance was compared with classical plug-in estimators and cross-validation strategies.

Main Results:

  • The proposed range-search technique demonstrates robustness to the Gaussianity assumption.
  • The method significantly improves accuracy compared to classical plug-in estimators.
  • It is comparable to cross-validation methods in accuracy but is tens to hundreds of times faster computationally.

Conclusions:

  • The developed range-search technique provides an accurate and computationally efficient solution for optimizing RLDA parameters.
  • This advancement is crucial for effective biomarker discovery in high-dimensional genomic datasets.
  • The source code is publicly available for further research and application.