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

Classifying gene expression profiles from pairwise mRNA comparisons.

Donald Geman1, Christian d'Avignon, Daniel Q Naiman

  • 1Center for Cardiovascular Bioinformatics and Modeling, Whitaker Biomedical Engineering Institute and Department of Applied Mathematics and Statistics, Johns Hopkins University, USA. ge-man@jhu.edu

Statistical Applications in Genetics and Molecular Biology
|May 2, 2006
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

Addressing Disparities: A Pragmatic Comparative Effectiveness Trial of School-Based Executive Functioning Interventions.

Evidence-based practice in child and adolescent mental health·2026
Same author

Mapping spatial gradients in spatial transcriptomics data with score matching.

bioRxiv : the preprint server for biology·2025
Same author

Label-free selection of marker genes in single-cell and spatial transcriptomics with geneCover.

Genome research·2025
Same author

Are NHANES Data Representative of the US Population for Chemicals with Seasonal and Regional Use?

Environmental health perspectives·2025
Same author

Development and Validation of Machine Learning Models for Adverse Events after Cardiac Surgery.

medRxiv : the preprint server for health sciences·2025
Same author

Predictive Analytics in Cardiothoracic Care: Enhancing Outcomes with the Healthcare Enabled by Artificial Intelligence in Real Time (HEART) Project.

Journal of Maine Medical Center·2025
Same journal

Annealed variational mixtures for disease subtyping and biomarker discovery.

Statistical applications in genetics and molecular biology·2026
Same journal

Performance of the permutation test approach with base calling errors for detecting changes in variant allele frequencies in ctDNA for a single patient.

Statistical applications in genetics and molecular biology·2026
Same journal

BLOG: Bayesian longitudinal omics with group constraints.

Statistical applications in genetics and molecular biology·2026
Same journal

AI-driven risk prediction and categorization in cystic fibrosis leveraging AttentiveLSTM and Fox Wolf Optimizer.

Statistical applications in genetics and molecular biology·2026
Same journal

Perfect collinearity not created equal: measuring and visualizing the severity of multi-collinearity of modern omics data.

Statistical applications in genetics and molecular biology·2026
Same journal

Corrigendum to: Choice of baseline hazards in joint modeling of longitudinal and time-to-event cancer survival data.

Statistical applications in genetics and molecular biology·2025
See all related articles

We developed a new Top-Scoring Pair (TSP) classifier for molecular classification using mRNA comparisons. This method offers accurate, transparent, and biologically meaningful predictions from gene expression data, improving disease detection and treatment response analysis.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene expression microarray data presents challenges for accurate statistical inference due to high dimensionality and limited sample sizes.
  • Conventional machine learning methods often yield complex, difficult-to-interpret classification models.
  • There is a need for transparent and biologically meaningful approaches to molecular classification.

Purpose of the Study:

  • To introduce a novel Top-Scoring Pair (TSP) classifier for molecular classification based on mRNA comparisons.
  • To address limitations of existing methods in analyzing gene expression data for disease detection, tumor identification, and treatment response prediction.
  • To provide a transparent and interpretable classification framework that generates testable biological hypotheses.

Main Methods:

Related Experiment Videos

  • The Top-Scoring Pair (TSP) classifier utilizes relative mRNA expression values between pairs of genes.
  • The method involves identifying gene pairs with the highest discriminatory power for classification.
  • It is a parameter-free approach, enhancing robustness and avoiding overfitting.

Main Results:

  • The TSP classifier achieves high prediction rates comparable to existing methods on standard cancer datasets.
  • The method involves a minimal number of genes and relies on relative expression, enhancing interpretability.
  • Decision rules generated by the TSP classifier offer specific hypotheses for biological validation.

Conclusions:

  • The TSP classifier provides an accurate, transparent, and biologically interpretable approach to molecular classification using gene expression data.
  • This method overcomes limitations of conventional techniques, offering improved class prediction for applications like disease detection and treatment response.
  • The parameter-free nature and hypothesis-generating capability make the TSP classifier a valuable tool for genomic research and clinical applications.