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Updated: Jun 17, 2026

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

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Published on: September 20, 2024

Biomarker identification by knowledge-driven multilevel ICA and motif analysis.

Li Chen¹, Jianhua Xuan, Chen Wang

¹Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA. lchen06@vt.edu

International Journal of Data Mining and Bioinformatics

|January 8, 2010

Summary

This study introduces knowledge-driven multi-level Independent Component Analysis (ICA) to find important ovarian cancer biomarkers from gene expression data, outperforming other methods.

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Published on: October 11, 2018

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Last Updated: Jun 17, 2026

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

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

Published on: September 20, 2024

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

Bioinformatics
Genomics
Systems Biology

Background:

Standard statistical methods struggle to identify significant biomarkers solely from gene expression data.
Prior biological knowledge is often underutilized in biomarker discovery from high-throughput data.

Purpose of the Study:

To develop a novel computational strategy for identifying biologically meaningful biomarkers.
To integrate clustering results and prior knowledge for enhanced biomarker discovery.
To infer underlying regulatory signals from gene expression data.

Main Methods:

Developed knowledge-driven multi-level Independent Component Analysis (ICA).
Incorporated clustering results and partial prior biological knowledge.
Designed a statistical test for transcription factor enrichment analysis using motif information.

Main Results:

Successfully extracted biologically meaningful biomarkers related to ovarian cancer.
Demonstrated superior performance compared to traditional gene selection methods.
Validated findings on an Rsf-1 (HBXAP) induced microarray dataset.

Conclusions:

Knowledge-driven multi-level ICA is effective for biomarker discovery in complex biological data.
The method enhances the identification of clinically relevant biomarkers.
Integrating prior knowledge significantly improves the biological interpretability of findings.