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

Building pathway clusters from Random Forests classification using class votes.

Herbert Pang1, Hongyu Zhao

  • 1Division of Biostatistics, Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06520, USA. herbert.pang@yale.edu

BMC Bioinformatics
|February 8, 2008
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

AI-powered digital innovations in pharmaceuticals research & development: Current landscape and case examples.

Journal of biopharmaceutical statistics·2026
Same author

Decentralized Clinical Trials in the Era of Real-World Evidence: A Critical Assessment of Recent Experiences.

Clinical and translational science·2025
Same author

rdborrow: an R package for causal inference incorporating external controls in randomized controlled trials with longitudinal outcomes.

Journal of biopharmaceutical statistics·2025
Same author

Multivariate time series clustering analysis of the Global Dietary Database to uncover patterns in dietary trends (1990-2018).

Public health nutrition·2025
Same author

Decentralized Clinical Trials in the Era of Real-World Evidence: A Statistical Perspective.

Clinical and translational science·2025
Same author

A causal inference framework for leveraging external controls in hybrid trials.

Biometrics·2024
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026
Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026
See all related articles

We developed a new method to cluster biological pathways based on gene expression data. This approach identifies groups of pathways related to specific traits, offering insights into molecular mechanisms and generating new hypotheses.

Area of Science:

  • Bioinformatics
  • Systems Biology
  • Genomics

Background:

  • Pathway-based methods are emerging for analyzing gene expression data, offering biological insights.
  • Understanding pathway coordination is crucial for cellular functions, yet methods for trait-related pathway clustering are lacking.

Purpose of the Study:

  • To develop and validate a novel method for constructing pathway clusters associated with specific biological traits.
  • To identify clusters of pathways with similar functions, regardless of shared genes, for deeper biological understanding.

Main Methods:

  • Pathway clustering using pathway-based classification methods.
  • Application to human breast cancer microarray datasets.
  • Validation using literature mining (PubMatrix) and network analysis (GeneGo MetaCore, Human Protein Reference Database).

Related Experiment Videos

Main Results:

  • The proposed method successfully identified consistent and interpretable pathway clusters across three breast cancer datasets.
  • Pathway clusters showed enrichment for genes associated with relevant keywords (e.g., estrogen receptor).
  • Network analysis revealed connections between pathways within clusters, even those without shared genes.

Conclusions:

  • The developed pathway clustering method enables investigation of gene relationships and pathway crosstalk.
  • This approach enhances understanding of molecular mechanisms underlying specific traits.
  • It facilitates the generation of novel biological hypotheses from gene expression data.