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Updated: Jun 4, 2026

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

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Omics-based identification of pathophysiological processes.

Hiroshi Tanaka¹, Soichi Ogishima

¹Department of Computational Biology, Graduate School of Biomedical Science, Tokyo Medical and Dental University, Tokyo, Japan. tanaka@cim.tmd.ac.jp

Methods in Molecular Biology (Clifton, N.J.)

|March 4, 2011

Summary

This summary is machine-generated.

Systems pathology views diseases as molecular network distortions. Omics technologies help identify these altered networks, classifying methods into data-inductive and knowledge-referenced approaches for clinical applications.

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Area of Science:

Biomedical research
Systems biology
Computational biology

Background:

Diseases are increasingly understood as disruptions in cellular molecular networks.
This perspective, termed "systems pathology," redefines disease understanding.

Purpose of the Study:

To discuss technologies and clinical applications for identifying disease-related molecular networks using Omics data.
To classify methods for Omics-based identification of pathophysiological processes.

Main Methods:

Data-inductive approaches: Inferring gene regulatory and transcriptional networks from gene expression data (e.g., DNA microarrays).
Knowledge-referenced approaches: Integrating differentially expressed genes with existing protein interaction networks or curated pathways.

Main Results:

The chapter details two primary classes of methods for analyzing molecular networks in disease.
Specific methods like ARACNe and eQTL are presented with their clinical applications.

Conclusions:

Omics data analysis provides powerful tools for understanding disease mechanisms at a systems level.
These approaches facilitate the identification of altered molecular networks, paving the way for new clinical applications.