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 Experiment Videos

Evaporative cooling feature selection for genotypic data involving interactions.

B A McKinney1, D M Reif, B C White

  • 1Department of Genetics, University of Alabama School of Medicine, Birmingham, AL 35294, USA. brett.mckinney@gmail.com

Bioinformatics (Oxford, England)
|June 26, 2007
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Uncovering Dental Caries Heterogeneity in NHANES Using Machine Learning.

Journal of dental research·2025
Same author

Centrality nearest-neighbor projected-distance regression (C-NPDR) feature selection for correlation-based predictors with application to resting-state fMRI study of major depressive disorder.

PloS one·2025
Same author

Continued Fever, or a So-Called Ridge Fever, in Upper South Carolina.

Atlanta journal-record of medicine·2022
Same author

Painless Vaccination.

Atlanta medical and surgical journal (1884)·2022
Same author

Association of hyperglycaemia with hospital mortality in nondiabetic COVID-19 patients: A cohort study.

Diabetes & metabolism·2021
Same author

Metabolomics Insights in Early Childhood Caries.

Journal of dental research·2021
Same journal

Cross-Domain Transfer Learning from Peptides to Metabolites Using a Multi-Property Fine-Tuned LLM.

Bioinformatics (Oxford, England)·2026
Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026
Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026
Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

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

We developed an evaporative cooling (EC) algorithm to identify key genetic variants for phenotype classification. EC effectively removes noise and identifies functional variations, even with complex interactions, improving genomic data analysis.

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Genome-wide genotyping presents challenges in identifying relevant genetic variants for phenotype classification, particularly with attribute interactions, noise, and small sample sizes.
  • Accurate identification of phenotype-associated genetic variations is crucial for understanding disease mechanisms and developing targeted therapies.

Purpose of the Study:

  • To introduce a novel feature selection algorithm, evaporative cooling (EC), for identifying minimal subsets of genetic variants relevant to phenotype classification.
  • To address the challenges posed by attribute interactions and noise in high-dimensional genomic data.

Main Methods:

  • The evaporative cooling (EC) algorithm utilizes an attribute quality measure analogous to thermodynamic free energy.

Related Experiment Videos

  • EC combines Relief-F and mutual information to remove noise features, analogous to physical evaporation.
  • The algorithm identifies a subset of attributes containing DNA sequence variations associated with a given phenotype.

    • Main Results:

    • EC successfully identifies functional sequence variations, including those involving epistatic interactions, which influence phenotype association.
    • The algorithm demonstrated effectiveness on both simulated genotypic data with attribute interactions and real-world data from individuals experiencing adverse events post-smallpox vaccination.
    • The EC formalism integrates information entropy, energy, and temperature into a single measure, balancing interaction and main effects.

    Conclusions:

    • Evaporative cooling (EC) is an effective method for selecting relevant genetic variants from complex, noisy, and high-dimensional genotypic data.
    • The algorithm's ability to handle attribute interactions and noise makes it valuable for phenotype classification in genomics.
    • Open-source software for the EC algorithm is available in Java.