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

Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
Human Genetics01:28

Human Genetics

Human genetics provides a profound framework for understanding the interplay between genetic predispositions and human psychology. At the heart of this discipline lies the study of how genes influence physical traits, behaviors, and susceptibility to diseases. Each person carries a unique genetic code that subtly or significantly shapes their psychological and behavioral landscape.
The complex relationship between genetics and psychology is observable through common biological components such...
Pharmacogenetics and Pharmacogenomics: Overview01:29

Pharmacogenetics and Pharmacogenomics: Overview

Pharmacogenetics and pharmacogenomics examine how genetic factors influence an individual's response to drugs. While pharmacogenetics focuses on the impact of specific genetic variants on drug effects, pharmacogenomics takes a broader approach, studying how genetic variation across populations contributes to differences in drug responses. These fields aim to explain why individuals may experience varying levels of efficacy or adverse reactions to the same medication.Variability in drug...
Principles of Pharmacogenetics: Types of Genetic Variants01:27

Principles of Pharmacogenetics: Types of Genetic Variants

The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...
Pharmacogenetic Phenotypes: Alterations in Pharmacokinetics, Drug Targets and Biologic Milieu01:29

Pharmacogenetic Phenotypes: Alterations in Pharmacokinetics, Drug Targets and Biologic Milieu

Genetic variations significantly influence drug response through pharmacokinetics, receptor interactions, and biologic milieu modifications. Pharmacokinetic alterations impact drug metabolism and clearance, affecting efficacy and toxicity. Variants in drug-metabolizing enzymes, such as CYP2C9 and CYP2C19, alter drug activation and elimination. For example, CYP2C9 loss-of-function variants require lower warfarin doses to prevent excessive bleeding, while CYP2C19 variants reduce clopidogrel...

You might also read

Related Articles

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

Sort by
Same author

A primer on the use of machine learning to distil knowledge from data in biological psychiatry.

Molecular psychiatry·2024
Same author

What makes a good prediction? Feature importance and beginning to open the black box of machine learning in genetics.

Human genetics·2021
Same author

Comparison of Juvenile Feed Protocols on Growth and Spawning in Zebrafish.

Journal of the American Association for Laboratory Animal Science : JAALAS·2021
Same author

A major role for common genetic variation in anxiety disorders.

Molecular psychiatry·2019
Same author

The role of polygenic risk score gene-set analysis in the context of the omnigenic model of schizophrenia.

Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology·2019
Same author

A review of neuroeconomic gameplay in psychiatric disorders.

Molecular psychiatry·2019
Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
Same journal

SpaMFG: a Spatial Multi-omics Integration Method based on Feature Grouping.

Bioinformatics (Oxford, England)·2026
Same journal

CSCN: Inference of Cell-Specific Causal Networks Using Single-Cell RNA-Seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

Sparse CCA-Based Mediation Analysis with High-Dimensional Exposures and Mediators.

Bioinformatics (Oxford, England)·2026
Same journal

Enhancing Cross-Context Generalization in Drug Perturbation Prediction with a Multimodal Conditional Diffusion Framework.

Bioinformatics (Oxford, England)·2026
Same journal

Primer Design through Submodular Function Estimation.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Jun 23, 2026

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

Predictor correlation impacts machine learning algorithms: implications for genomic studies.

Kristin K Nicodemus1, James D Malley

  • 1Department of Statistical Genetics, Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK. kristin.nicodemus@well.ox.ac.uk

Bioinformatics (Oxford, England)
|May 23, 2009
PubMed
Summary
This summary is machine-generated.

Correlations among predictors in high-dimensional genomic data can bias variable importance measures from machine learning algorithms (MLAs). Permutation-based variable importance measures are recommended for correlated predictors to ensure valid inferences.

More Related Videos

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Related Experiment Videos

Last Updated: Jun 23, 2026

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • High-throughput genomics generates large datasets with numerous predictors.
  • Machine learning algorithms (MLAs) efficiently identify phenotype-associated variables in high-dimensional data.
  • Correlations among predictors in genomic data present challenges for MLAs.

Purpose of the Study:

  • To investigate the impact of predictor correlations on variable importance measures (VIMs) from three MLAs: random forest (RF), conditional inference forest (CIF), and Monte Carlo logic regression (MCLR).
  • To evaluate the validity of inferences drawn from VIMs in the presence of correlated predictors.

Main Methods:

  • Extensive simulations were conducted to assess VIMs under varying degrees of predictor correlation.
  • A case-control illustration was used to demonstrate the performance of RF VIMs in detecting associations with correlated predictors.
  • Analysis focused on RF Gini index and permutation-based VIMs.

Main Results:

  • Considering predictor correlation is crucial for valid inferences using VIMs from RF, CIF, and MCLR.
  • RF VIMs, including permutation-based ones, showed reduced ability to detect associations when causal predictors were correlated with others.
  • RF Gini VIMs are biased under correlation, dependent on correlation strength and number, and prone to overfitting with small terminal node sizes.
  • Permutation-based VIM distributions were less variable and unbiased for correlated predictors, suggesting their preference in such scenarios.

Conclusions:

  • MLAs are powerful for high-dimensional data but require careful application, especially concerning predictor correlations.
  • Permutation-based VIMs offer a more reliable approach for variable importance assessment when predictors are correlated.
  • Valid conclusions from genomic studies using MLAs necessitate addressing predictor correlation issues.