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Updated: Jan 12, 2026
22:27
Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.
Published on: May 6, 2010
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Probing omics data via harmonic persistent homology
Davide Gurnari1, Aldo Guzmán-Sáenz2, Filippo Utro2
1Dioscuri Centre in Topological Data Analysis, Mathematical Institute PAN, Warsaw, Poland.
Scientific Reports
|November 6, 2025
Summary
Topological data analysis (TDA) using harmonic persistent homology can identify molecular biomarkers in complex diseases. This method reveals hidden patterns in multi-omics data for disease subtyping and biomarker discovery.
Area of Science:
- Computational Biology
- Data Science
- Genomics
Background:
- Identifying molecular signatures in complex diseases with similar symptoms from high-dimensional multi-omics data is challenging.
- Topological Data Analysis (TDA) offers a geometric approach to extract higher-order relationships from data.
Purpose of the Study:
- To apply harmonic persistent homology to multi-omics data for improved biomarker identification.
- To overcome limitations in cycle representation within TDA for homology classes.
Main Methods:
- Utilized harmonic persistent homology, a TDA technique, on multi-omics datasets.
- Applied the method to analyze the geometric structure of high-dimensional data.
Main Results:
- Discovered hidden patterns and relationships between different omic profiles.
- Successfully identified biomarkers predictive of disease subtypes in cancer data.
- Demonstrated effective dissection of multi-omics data for biomarker discovery.
Conclusions:
- Harmonic persistent homology is effective for multi-omics data analysis.
- This approach aids in disease subtyping and identifying biomarkers for complex diseases.
- The method highlights latent biological pathways associated with disease.
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