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

Genetic Screens02:46

Genetic Screens

4.9K
Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...
4.9K
Genetic Lingo01:11

Genetic Lingo

101.1K
Overview
101.1K
Combinatorial Gene Control02:33

Combinatorial Gene Control

8.3K
Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
8.3K
Multiple Regression01:25

Multiple Regression

2.9K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
2.9K
Polygenic Traits01:18

Polygenic Traits

64.8K
When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
64.8K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.5K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.5K

You might also read

Related Articles

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

Sort by
Same author

Joule-heating synthesis of high-entropy oxides as efficient catalysts for electrochemical methanol oxidation.

Chemical communications (Cambridge, England)·2026
Same author

Mesonephric-like adenocarcinoma of the uterine corpus: a case report.

Frontiers in medicine·2026
Same author

WNT4 reprograms dental pulp stem cells to resist PANoptosis and rebuild neurogenic potential for facial nerve injury repair.

Inflammation research : official journal of the European Histamine Research Society ... [et al.]·2026
Same author

Monte Carlo investigation of spatiotemporal distortions in attosecond soft X-ray pulse focusing using a two-stage toroidal mirror system.

Optics express·2026
Same author

Integrated Analysis Identifies an Anoikis-Related Gene Signature for Predicting Prognosis in Patients With Triple-Negative Breast Cancer.

IET systems biology·2026
Same author

Multimodal interventional bronchoscopy for chronic pulmonary <i>Aspergillus</i> infection with post-tubercular bronchial occlusion: a case report.

Frontiers in medicine·2026
Same journal

Computing Optimal Populations for Binary Problems using Logic Minimization.

Evolutionary computation·2026
Same journal

Enhancing Generalization and Scalability for Multi-Objective Optimization with Population Pre-Training.

Evolutionary computation·2026
Same journal

XCS for Sequential Perceptual Aliasing in Multi-Step Decision Making.

Evolutionary computation·2026
Same journal

A dynamic multi-objective evolutionary algorithm using dual-space prediction and surrogate-based sampling.

Evolutionary computation·2026
Same journal

Adapting MOEA/D to CMA-ES for Dealing with Ill-conditioned Multiobjective Problems.

Evolutionary computation·2026
Same journal

Editorial of the Special Issue: Parallel Problem Solving from Nature PPSN 2024 Extended Versions of Best Paper Candidates.

Evolutionary computation·2026
See all related articles

Related Experiment Video

Updated: Jun 7, 2025

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

7.4K

Genetic Programming-based Feature Selection for Symbolic Regression on Incomplete Data.

Baligh Al-Helali1, Qi Chen2, Bing Xue3

  • 1School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington 6140, New Zealand baligh.al-helali@ecs.vuw.ac.nz.

Evolutionary Computation
|November 21, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a genetic programming approach for symbolic regression on incomplete, high-dimensional data. The method effectively handles missing values and irrelevant features, improving accuracy and efficiency.

Keywords:
Feature SelectionGenetic ProgrammingHigh-dimensionalityIncomplete DataSymbolic Regression

More Related Videos

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

641
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.1K

Related Experiment Videos

Last Updated: Jun 7, 2025

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

7.4K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

641
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.1K

Area of Science:

  • Machine Learning
  • Data Science
  • Computational Intelligence

Background:

  • High-dimensionality and incomplete data present significant challenges in symbolic regression.
  • Traditional genetic programming requires data imputation, which can be inefficient for irrelevant features.

Purpose of the Study:

  • To develop a genetic programming-based method for feature selection directly from incomplete high-dimensional data.
  • To enhance symbolic regression performance by efficiently handling missing values and irrelevant features.

Main Methods:

  • Extending genetic programming with identity/neutral elements in function operators to manage missing data.
  • Implementing a novel approach for direct feature selection from incomplete datasets.

Main Results:

  • The proposed method outperforms state-of-the-art techniques on high-dimensional symbolic regression tasks with incomplete data.
  • Achieved superior symbolic regression accuracy and identified a smaller subset of relevant features.

Conclusions:

  • The novel genetic programming approach effectively addresses challenges of incomplete, high-dimensional data in symbolic regression.
  • The method improves both the accuracy and efficiency of the symbolic regression learning process.