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

Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
Health Information Technology and Healthcare Information System01:30

Health Information Technology and Healthcare Information System

Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:

You might also read

Related Articles

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

Sort by
Same author

An attention-infused deep convolutional paradigm for multi-label classification of thoracic pathologies in chest radiographs.

Scientific reports·2026
Same author

Case Report: Duodenal Strongyloidiasis - A Rare Cause of Melena.

GE Portuguese journal of gastroenterology·2026
Same author

Entropy-Engineered Catalysts for Electrochemical Nitrate Reduction to Ammonia.

Chemical record (New York, N.Y.)·2026
Same author

Exploring the Antidiabetic Potential of Achyranthes aspera: Bioactive Compound Extraction, In vitro Efficacy, and In silico Insights for Glycemic Control.

Current pharmaceutical biotechnology·2026
Same author

Clinical Predictors of In-Hospital Mortality among Patients with Stent Thrombosis Following Primary Percutaneous Coronary Intervention Using Drug-Eluting Stents.

Journal of the College of Physicians and Surgeons--Pakistan : JCPSP·2026
Same author

AI-driven optimization in cloud computing: a systematic review of cost, resource management, and security.

Frontiers in artificial intelligence·2026

Related Experiment Video

Updated: May 22, 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

7.4K

Optimal features selection in the high dimensional data based on robust technique: Application to different health

Ibrar Hussain1, Moiz Qureshi2,3, Muhammad Ismail4,3

  • 1Department of Statistics Abdul Wali Khan University Mardan, Pakistan.

Heliyon
|September 19, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a hybrid gene selection method combining Signal-to-Noise Ratio and Mood median test for high-dimensional bioinformatics data. The approach effectively identifies key genes, improving classification accuracy and reducing errors in Random Forest and KNN models.

Keywords:
High-dimensional dataHybrid techniqueMachine learning modelsMood median testOptimizing gene selectionSingle noise ratio score

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.7K
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

655

Related Experiment Videos

Last Updated: May 22, 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

7.4K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.7K
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

655

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Statistical Genetics

Background:

  • High-dimensional data in bioinformatics, especially from microarrays, presents challenges due to numerous genes and few samples.
  • Redundant genes obscure important biological signals, complicating accurate classification and increasing generalization error.
  • Effective gene selection is crucial for reducing dimensionality and enhancing the performance of machine learning models.

Purpose of the Study:

  • To develop and evaluate a novel hybrid gene selection approach for high-dimensional biological data.
  • To integrate the Signal-to-Noise Ratio (SNR) with the Mood median test for robust gene identification.
  • To assess the efficacy of the selected genes in improving classification accuracy using Random Forest and KNN.

Main Methods:

  • A hybrid gene selection strategy combining the Signal-to-Noise Ratio (SNR) score and the Mood median test.
  • The Mood median test was employed to handle non-normal or skewed data and identify significant gene changes.
  • A novel 'Md score' was calculated by dividing the SNR value by the gene's significant P-value from the Mood median test.

Main Results:

  • The hybrid approach successfully identified significant genes with high classification importance and low noise.
  • Classification using Random Forest and K-Nearest Neighbors (KNN) with selected genes demonstrated improved accuracy.
  • The proposed method achieved lower classification error rates compared to conventional gene selection techniques on high-dimensional datasets.

Conclusions:

  • The hybrid gene selection method offers a robust and effective solution for high-dimensional bioinformatics data.
  • This approach enhances classification performance by selecting biologically relevant and statistically significant genes.
  • The findings suggest this technique is a valuable tool for improving gene selection in complex biological analyses.