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

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...
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

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...
Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
Quadratic Models01:23

Quadratic Models

Quadratic models are mathematical representations used to describe relationships in which the rate of change changes at a constant rate. These models appear in a wide variety of natural and engineered systems, especially those involving motion, forces, and optimization. One common application is analyzing the vertical motion of objects influenced by gravity, such as a ball thrown into the air.In such scenarios, the object's height changes over time in a curved pattern, rising to a maximum point...
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...
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...

You might also read

Related Articles

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

Sort by
Same author

Deep learning based attention enhanced phylogenetic radial basis function networks (AE-PRBFN) for genomic codon usage classification across species.

Scientific reports·2026
Same author

Regularized regression in ultra-small chemometric datasets: A methodological case study using FTIR spectra of Schiff bases.

PloS one·2026
Same author

Circulating tumor cells and tumor-derived cell-free DNA in cancer management: Clinical evidence, limitations, and future directions.

The International journal of biological markers·2026
Same author

Predicting sediment ecological state from metagenomes shows equal performance for taxonomic and functional features.

Marine environmental research·2026
Same author

Enhancing Strength and Ductility of Rubberized Concrete Using Low-Cost Glass Jackets.

Polymers·2026
Same author

Synergistic antidiabetic effects of GC-MS profiled <i>Prunus persica</i> kernel extract and chromium picolinate in alloxan-induced diabetic rats.

European journal of mass spectrometry (Chichester, England)·2026
Same journal

Haplotype-aware long-read error correction.

Algorithms for molecular biology : AMB·2026
Same journal

Extension of partial atom-to-atom maps: uniqueness and algorithms.

Algorithms for molecular biology : AMB·2026
Same journal

Lossless pangenome indexing using tag arrays.

Algorithms for molecular biology : AMB·2026
Same journal

Dolphyin: a combinatorial algorithm for identifying 1-Dollo phylogenies in cancer.

Algorithms for molecular biology : AMB·2026
Same journal

Probing transcription factor subsets in gene regulatory networks.

Algorithms for molecular biology : AMB·2026
Same journal

Comparing the ability of embedding methods on metabolic hypergraphs for capturing taxonomy-based features.

Algorithms for molecular biology : AMB·2026
See all related articles

Related Experiment Video

Updated: May 26, 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

A Partial Least Squares based algorithm for parsimonious variable selection.

Tahir Mehmood1, Harald Martens, Solve Sæbø

  • 1Biostatistics, Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, Norway. tahir.mehmood@umb.no.

Algorithms for Molecular Biology : AMB
|December 7, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new algorithm for genomics that selects fewer variables for better model understanding and consistency. It balances model simplicity with predictive performance, outperforming standard methods.

More Related Videos

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
06:50

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

Published on: November 8, 2019

Related Experiment Videos

Last Updated: May 26, 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

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
06:50

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

Published on: November 8, 2019

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Genomics research often requires selecting a subset of variables from large datasets.
  • Identifying associations between variables (e.g., codon usage and taxonomy) is crucial.
  • Key challenges include maximizing understandability, ensuring variable consistency, and maintaining model performance.

Purpose of the Study:

  • To develop an algorithm that balances model parsimony and predictive performance.
  • To improve the understandability of genomic models by reducing the number of selected variables.
  • To apply and evaluate the algorithm for taxonomic classification using codon variations.

Main Methods:

  • Variable selection using reduced-rank Partial Least Squares (PLS) with regularized elimination.
  • Comparison of three PLS variable selection criteria: loading weights, regression coefficients, and variable importance on projections.
  • Application to identify codon variations for bacterial taxa discrimination.

Main Results:

  • The proposed algorithm significantly reduces the number of selected variables with only a marginal decrease in model performance.
  • This reduction enhances model understandability and consistency.
  • The algorithm demonstrated competitive or superior performance compared to forward selection, Lasso, and Soft-threshold PLS.

Conclusions:

  • A regularized elimination algorithm based on PLS improves model understandability and consistency.
  • The algorithm effectively reduces classification error on test data compared to standard methods.
  • This approach offers a valuable tool for variable selection in genomic and metagenomic analyses.