Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Videos

Integrating functional knowledge during sample clustering for microarray data using unsupervised decision trees.

Henning Redestig¹, Dirk Repsilber, Florian Sohler

¹Max Planck Institute for Molecular Plant Physiology, Am Mühlenberg 1, 14476 Golm, Germany. redestig@mpimp-golm.mpg.de

Biometrical Journal. Biometrische Zeitschrift

|May 5, 2007

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

Effect-based spatiotemporal assessment of suspended particulate matter in the River Rhine: An early warning platform for environmental monitoring.

Journal of hazardous materials·2026

Same author

A novel diagnostic serum protein signature for pediatric inflammatory bowel disease.

Journal of pediatric gastroenterology and nutrition·2026

Same author

Effects of incrementally increased plant-based protein intake on gut microbiota and inflammatory-metabolic biomarkers in healthy adults.

Food & function·2026

Same author

Acute effects of butyrate on intestinal permeability in patients with irritable bowel syndrome assessed by a novel colonoscopy research model.

Gut microbes·2025

Same author

Transcriptional modulation unique to vulnerable motor neurons predicts ALS across species and SOD1 mutations.

Genome research·2025

Same author

Results of the Protein Engineering Tournament: An Open Science Benchmark for Protein Modeling and Design.

Proteins·2025

Same journal

Comparison of Different Methods for the Meta-Analysis of Diagnostic Test Accuracy Studies-A Simulation Study.

Biometrical journal. Biometrische Zeitschrift·2026

Same journal

When to Adjust for Multiple Testing: A Unifying Guiding Principle.

Biometrical journal. Biometrische Zeitschrift·2026

Same journal

Ensuring Quality in Preclinical Research: The Importance of Being Human.

Biometrical journal. Biometrische Zeitschrift·2026

Same journal

Addressing Cluster-Level Treatment Effect Heterogeneity in Sample Size Determination for Hierarchical 2 × 2 Factorial Designs.

Biometrical journal. Biometrische Zeitschrift·2026

Same journal

A Multiple Imputation Approach to Distinguish Curative From Life-Prolonging Effects in the Presence of Missing Covariates.

Biometrical journal. Biometrische Zeitschrift·2026

Same journal

Tests for Categorical Data Beyond Pearson: A Distance Covariance and Energy Distance Approach.

Biometrical journal. Biometrische Zeitschrift·2026

See all related articles

This study introduces a novel gene expression data clustering method integrating prior biological knowledge. This approach enhances the biological interpretation of sample groupings and potentially improves clustering accuracy.

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Clustering gene expression data is crucial for identifying sample groups and biological insights.
Traditional methods often filter high-variance genes, potentially losing important biological information.
Existing clustering techniques lack direct biological interpretation.

Purpose of the Study:

To develop a novel method for clustering gene expression data that integrates prior biological information.
To improve the biological interpretability and performance of sample clustering.
To create a dendrogram-based approach resembling decision trees for biological interpretation.

Main Methods:

Integrated prior biological knowledge directly into the clustering algorithm.

Related Experiment Videos

Utilized gene classes to split data at each node of a dendrogram.

Developed a method that generates interpretable, decision-tree-like dendrograms.

Main Results:

The proposed method demonstrated usefulness on both simulated and real gene expression data.
The approach facilitates finding biologically meaningful differences between sample groups.
Successful integration of biological knowledge improved clustering interpretability.

Conclusions:

The novel method serves as a valuable complementary tool for gene expression data analysis.
It is particularly effective when assumptions of few differentially expressed genes and informative gene class mappings are met.
This approach enhances biological interpretation and potentially boosts clustering performance.