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

Molecular decomposition of complex clinical phenotypes using biologically structured analysis of microarray data.

Claudio Lottaz1, Rainer Spang

  • 1Max Planck Institute for Molecular Genetics and Berlin Center for Genome Based Bioinformatics, Germany. Claudio.Lottaz@molgen.mpg.de

Bioinformatics (Oxford, England)
|January 29, 2005
PubMed
Summary

This study introduces Structured Analysis of Microarrays (StAM), a novel algorithm for analyzing complex clinical phenotypes. StAM addresses molecular heterogeneity by integrating gene expression data with Gene Ontology annotations to identify disease subentities.

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

Mantle cell lymphoma artificial intelligence prognostic index using hematoxylin and eosin histology.

Leukemia·2026
Same author

<b>mhn</b>: a Python package for analyzing cancer progression with Mutual Hazard Networks.

Bioinformatics advances·2026
Same author

Integration of high-throughput proteomic data and complementary omics layers with PriOmics.

Genome research·2025
Same author

Lipid metabolism of clear cell renal cell carcinoma predicts survival and affects intratumoral CD8 T cells.

Translational oncology·2025
Same author

Harp: data harmonization for computational tissue deconvolution across diverse transcriptomics platforms.

Bioinformatics (Oxford, England)·2025
Same author

Virtual tissue expression analysis.

Bioinformatics (Oxford, England)·2024

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene-expression patterns are crucial for characterizing clinical phenotypes in research.
  • Complex phenotypes present challenges due to molecular heterogeneity, where diverse molecular disorders can cause similar clinical presentations.
  • Existing methods may not systematically address this molecular diversity in gene-expression analysis.

Purpose of the Study:

  • To develop a novel algorithm, Structured Analysis of Microarrays (StAM), to address molecular heterogeneity in complex clinical phenotypes.
  • To improve the characterization of clinical phenotypes by integrating gene expression data with functional annotations.
  • To uncover potential molecular disease subentities and their associated biological processes.

Main Methods:

Related Experiment Videos

  • Developed the Structured Analysis of Microarrays (StAM) algorithm.
  • Utilized gene expression data alongside functional annotations from the Gene Ontology database.
  • Built biologically focused classifiers to analyze molecular heterogeneity.

Main Results:

  • The StAM algorithm effectively accounts for molecular heterogeneity in complex clinical phenotypes.
  • The approach integrates gene expression data and Gene Ontology annotations to build robust classifiers.
  • Identified potential molecular disease subentities and linked them to specific biological processes without reducing prediction accuracy.

Conclusions:

  • StAM offers a novel approach to analyzing complex clinical phenotypes with molecular heterogeneity.
  • The algorithm enhances biological insight by linking molecular subentities to biological processes.
  • This method provides a powerful tool for clinical research, improving phenotype characterization.