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 Video

Updated: Jun 5, 2026

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Confident predictability: identifying reliable gene expression patterns for individualized tumor classification using

Lee K Jones1, Fei Zou, Alexander Kheifets

  • 1Department of Mathematical Sciences, University of Massachusetts, Lowell, MA, USA. Lee_Jones@UML.edu

BMC Medical Genomics
|January 26, 2011
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

irAE.AI: AI-powered exploration of real-world immune-related adverse events.

JAMIA open·2026
Same author

Targeting of MEK and Autophagy in Pancreatic Adenocarcinoma and Analysis of Treatment Sensitivity in Preclinical and Clinical Models: MEKiAUTO.

JCO precision oncology·2026
Same author

Clinico-genomic nomograms for estimation of survival risk in metastatic castrate-resistant prostate cancer.

JNCI cancer spectrum·2026
Same author

Associations Between Two-Year Immune-Related Adverse Events and Psychological Distress and Subsequent Survival Outcomes Among ICI-Treated Survivors Diagnosed With Advanced Cancers in the US.

Psycho-oncology·2026
Same author

Impact of baseline medications on real-world overall survival in immune checkpoint inhibitor-treated patients with cancer in the RADIOHEAD cohort.

Med (New York, N.Y.)·2026
Same author

A Strategic Partnership to Advance AI Applications in Genomics and Bioinformatics for Health Innovation.

JMIR bioinformatics and biotechnology·2026

This study introduces a novel machine learning approach for molecular tumor classification using gene expression data. The method enhances prediction accuracy for individual patients, enabling confident treatment decisions.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Molecular tumor classification relies on gene expression profiling.
  • Traditional machine learning algorithms offer population-level error rates, lacking individual patient accuracy.
  • Local minimax learning provides individual probability estimates with error bounds.

Purpose of the Study:

  • To develop a novel machine learning method for accurate molecular tumor classification.
  • To provide individualized confidence levels for patient predictions.
  • To improve the translation of gene expression signatures into clinical practice.

Main Methods:

  • Feature selection using the k-TSP algorithm.
  • Classifier construction via local minimax kernel learning.

Related Experiment Videos

Last Updated: Jun 5, 2026

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

  • Testing on three publicly available gene expression datasets.

    • Main Results:

    • Achieved significantly lower error rates on identifiable patient subsets.
    • Developed interpretable classifiers with individualized confidence levels.
    • Demonstrated improved prediction accuracy for individual patients.

    Conclusions:

    • Confident predictions enable timely treatment for cancer patients and prevent unnecessary treatment for healthy individuals.
    • The method facilitates the clinical application of gene expression signatures for personalized medicine.
    • Offers a valuable tool for translating complex genomic data into actionable clinical insights.