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

You might also read

Related Articles

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

Sort by
Same author

Structural Modification and Development of <i>N</i>-(1,2,3,4-Tetrahydro-3-isoquinolinylmethyl)benzamide, BPR1M492, as a Potent and Rapid-Onset Opioid Analgesic with Reduced Withdrawal Symptoms.

Journal of medicinal chemistry·2026
Same author

Integrating machine learning and protein-ligand interaction profiling for the discovery of METTL3 inhibitors.

Scientific reports·2025
Same author

Fipronil Triggers Immunotoxicity Through Reactive Oxygen Species-Driven Mitochondrial Apoptosis in Thymocytes.

Toxics·2025
Same author

Orally Bioavailable and Site-Selective Covalent STING Inhibitor Derived from a Macrocyclic Marine Diterpenoid.

Journal of medicinal chemistry·2025
Same author

An Integrated Testing Strategy and Online Tool for Assessing Skin Sensitization of Agrochemical Formulations.

Toxics·2025
Same author

Incorporating Tissue-Specific Gene Expression Data to Improve Chemical-Disease Inference of in Silico Toxicogenomics Methods.

Journal of xenobiotics·2024

Related Experiment Video

Updated: Sep 26, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K

A Machine Learning Classifier for Predicting Stable MCI Patients Using Gene Biomarkers.

Run-Hsin Lin1,2, Chia-Chi Wang3, Chun-Wei Tung1,2

  • 1Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, Miaoli County 35053, Taiwan.

International Journal of Environmental Research and Public Health
|April 23, 2022
PubMed
Summary

This study identifies 29 blood gene biomarkers to predict stable mild cognitive impairment (MCI) patients, potentially delaying Alzheimer's disease progression. The developed machine learning model offers a low-invasive, cost-efficient screening tool for early diagnosis.

Keywords:
Alzheimer’s diseasefeature selectiongene biomarkersmild cognitive impairmentrandom forest

More Related Videos

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

7.7K
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

883

Related Experiment Videos

Last Updated: Sep 26, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K
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

7.7K
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

883

Area of Science:

  • Neuroscience
  • Genetics
  • Biomarker Discovery

Background:

  • Alzheimer's disease (AD) is an irreversible neurodegenerative disorder.
  • Mild cognitive impairment (MCI) patients have a high risk of progressing to AD.
  • Current AD diagnostic methods are invasive or costly, limiting widespread use.

Purpose of the Study:

  • To develop a low-invasive, cost-efficient screening method for identifying stable MCI patients.
  • To identify blood-sample gene biomarkers for predicting stable MCI.
  • To aid in early diagnosis and slow AD progression.

Main Methods:

  • Utilized two datasets from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.
  • Applied feature selection and machine learning algorithms.
  • Developed a random forest-based classifier to predict stable MCI patients using gene biomarkers.

Main Results:

  • Identified 29 gene biomarkers (31 probes) for predicting stable MCI.
  • Achieved an AUC of 0.841 (cross-validation) and 0.775 (test set) with the random forest classifier.
  • Demonstrated 97% concordance for prediction scores > 0.9, indicating high accuracy in identifying stable MCI patients.

Conclusions:

  • The proposed prediction model effectively identifies stable MCI patients using blood gene biomarkers.
  • This method offers a novel, cost-efficient, and low-invasive first-tier diagnostic option.
  • The findings support precision medicine approaches for early AD intervention.