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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

290
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...
290
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

249
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...
249
Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

1.6K
Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
1.6K
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

4.1K
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...
4.1K

You might also read

Related Articles

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

Sort by
Same author

Association Between Serum HBV DNA Levels and CCL-20, CD8a, CXCL-16, and GDF-15 in Patients with Chronic Hepatitis B.

Viruses·2025
Same author

Development of the National Adult Reading Test-Türkiye (NART-TR) for practical assessment of mental capacity.

The Clinical neuropsychologist·2025
Same author

Validation and Translation of the 3D-CAM to Turkish in Surgical Intensive Care Patients.

Turkish journal of anaesthesiology and reanimation·2025
Same author

Body Mass Index Growth Curves for Birth to 24 Months Children in Ankara with RefCurv Software

Balkan medical journal·2023
Same author

Evaluation of rhomboid intercostal and subserratus plane block under direct vision for postoperative analgesia in thoracic surgeries: a prospective, randomized controlled trial.

European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery·2022
Same author

Abnormal quantitative pupillary light responses following COVID-19.

International ophthalmology·2022

Related Experiment Video

Updated: Jan 16, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K

Entropy Methods on Finding Optimal Linear Combinations with an Application to Biomarkers.

Mehmet Sinan İyisoy1, Pınar Özdemir2

  • 1Department of Medical Education and Informatics, Necmettin Erbakan University, Konya 42090, Turkey.

Entropy (Basel, Switzerland)
|September 27, 2025
PubMed
Summary

This study introduces novel information-theoretical methods to optimize linear combinations of continuous variables, like biomarkers, for improved diagnostic accuracy in medicine. These approaches outperform traditional logistic regression in simulations and real-world data analysis.

Keywords:
binary outcomebiomarkerslinear combination

More Related Videos

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data
04:57

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

Published on: May 16, 2022

17.3K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.1K

Related Experiment Videos

Last Updated: Jan 16, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K
Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data
04:57

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

Published on: May 16, 2022

17.3K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.1K

Area of Science:

  • Biostatistics
  • Medical Informatics
  • Machine Learning

Background:

  • Optimal linear combinations of continuous variables are crucial in medicine for diagnosis.
  • Existing methods often rely on logistic regression coefficients, serving as a benchmark.
  • Biomarker combinations are essential when individual markers lack diagnostic power.

Purpose of the Study:

  • To propose and evaluate novel information-theoretical approaches for determining optimal linear combination coefficients.
  • To enhance the diagnostic utility of continuous variables, particularly biomarkers.
  • To compare the performance of new methods against logistic regression-based combinations.

Main Methods:

  • Utilizing information-theoretical objective functions to derive linear combination coefficients.
  • Applying these novel methods to biomarker combination problems.
  • Assessing performance using Area Under the ROC Curve (AUC) and other metrics.

Main Results:

  • The proposed information-theoretical methods demonstrate superior performance compared to logistic regression coefficients.
  • The novel approaches show significant improvements in diagnostic accuracy for biomarker combinations.
  • Validation through extensive simulations and a real-life data application.

Conclusions:

  • Information-theoretical optimization offers a powerful new framework for creating effective linear combinations of continuous variables.
  • These methods provide a valuable alternative to traditional approaches, especially in biomarker-driven diagnostics.
  • The study highlights the potential of optimizing information-theoretic objectives for enhanced medical diagnosis.