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

A Hybrid Preprocessing Multi-Objective Surrogate Model for Thermal MEMS Actuators.

Micromachines·2026
Same author

Jasmonate, salicylate, and ethylene-responsive transcriptomics discovery in spikelets of three wheat genotypes reveals a rapid and conserved response for jasmonate signaling.

Plant signaling & behavior·2026
Same author

Environmental assessment of tide-driven spatiotemporal dynamics of fecal coliform as a microbial hazard along Canada's coasts.

The Science of the total environment·2026
Same author

Nanoparticles isolated from the Gubi Zhitong formula alleviate knee osteoarthritis by reprogramming macrophages.

Materials today. Bio·2026
Same author

Tracking global quality of life trajectories in knee osteoarthritis: a population-based long-term analysis.

Therapeutic advances in musculoskeletal disease·2026
Same author

Evaluation and identification of anthracnose resistance in pumpkin germplasm.

Plant disease·2026
Same journal

Interpretable machine learning for Parkinson's disease diagnosis, staging, and biological mechanism exploration: a multicenter analysis.

BioData mining·2026
Same journal

Learning a distance for the clustering of patients with amyotrophic lateral sclerosis.

BioData mining·2026
Same journal

Multi-domain feature fusion with variational mode decomposition and hybrid LightGBM-Logistic Regression for multi-class seizure classification.

BioData mining·2026
Same journal

Large-scale transcriptomic data mining using explainable XGBoost and SHAP reveals shared biomarkers and molecular mechanisms between type-2 diabetes and triple-negative breast cancer for drug repurposing.

BioData mining·2026
Same journal

AVSeg-XAI: Deep learning framework for A/V segmentation with vascular features reveals retinal oculomics as biomarker for cardiovascular disease.

BioData mining·2026
Same journal

Navigating the uncharted: AI-driven advances in protein structure, dynamics, interactions and ligand interactions for understudied families.

BioData mining·2026
See all related articles

Related Experiment Video

Updated: Jan 12, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

4.0K

Semi-supervised consensus clustering for gene expression data analysis.

Yunli Wang1, Youlian Pan2

  • 1National Research Council Canada, 46 Dineen Dr., Fredericton, Canada.

Biodata Mining
|June 13, 2014
PubMed
Summary
This summary is machine-generated.

Semi-supervised consensus clustering (SSCC) enhances gene expression data analysis by integrating prior knowledge and consensus clustering. This approach improves robustness and accuracy, outperforming traditional methods in handling noisy, high-dimensional microarray data.

Keywords:
Consensus clusteringGene expressionSemi-supervised clusteringSemi-supervised consensus clustering

More Related Videos

Low-input Nucleus Isolation and Multiplexing with Barcoded Antibodies of Mouse Sympathetic Ganglia for Single-nucleus RNA Sequencing
10:44

Low-input Nucleus Isolation and Multiplexing with Barcoded Antibodies of Mouse Sympathetic Ganglia for Single-nucleus RNA Sequencing

Published on: March 23, 2022

4.8K
JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.6K

Related Experiment Videos

Last Updated: Jan 12, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

4.0K
Low-input Nucleus Isolation and Multiplexing with Barcoded Antibodies of Mouse Sympathetic Ganglia for Single-nucleus RNA Sequencing
10:44

Low-input Nucleus Isolation and Multiplexing with Barcoded Antibodies of Mouse Sympathetic Ganglia for Single-nucleus RNA Sequencing

Published on: March 23, 2022

4.8K
JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.6K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Traditional clustering methods like k-means struggle with noisy and high-dimensional gene expression data.
  • Consensus clustering enhances result robustness, while semi-supervised clustering integrates prior biological knowledge.
  • Existing methods often fail to adequately address the complexities of microarray data.

Purpose of the Study:

  • To develop and evaluate a novel semi-supervised consensus clustering (SSCC) method for gene expression data analysis.
  • To investigate the combined benefits of consensus clustering and prior knowledge integration.
  • To compare SSCC performance against standard and existing advanced clustering algorithms.

Main Methods:

  • Proposed the semi-supervised consensus clustering (SSCC) algorithm.
  • Integrated consensus clustering principles with semi-supervised learning techniques.
  • Performed comparative analysis using eight gene expression datasets with h-fold cross-validation.

Main Results:

  • Prior knowledge incorporation significantly improved clustering quality by mitigating noise and dimensionality.
  • The integration of consensus and semi-supervised clustering yielded superior performance over individual approaches.
  • SSCC demonstrated superior performance compared to k-means, a standalone semi-supervised method, and a consensus clustering algorithm.

Conclusions:

  • Semi-supervised consensus clustering (SSCC) effectively enhances gene expression data analysis.
  • The SSCC method offers improved robustness and accuracy for microarray data.
  • Integrating prior knowledge with consensus clustering is a promising strategy for complex biological data.