ArticleScientific reports2025
Probing omics data via harmonic persistent homology.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Advancing single-cell omics and cell-based therapeutics with quantum computing.Nature reviews. Molecular cell biology · 2026Review
- Uncovering the Understanding of the Concept of Patient Similarity in Cancer Research and Treatment: Scoping Review.Journal of medical Internet research · 2025Article
- A cohomology-based Gromov-Hausdorff metric approach for quantifying molecular similarity.Scientific reports · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
Identifying molecular signatures from complex disease patients with underlying symptomatic similarities is a significant challenge in the analysis of high-dimensional multi-omics data. Topological data analysis (TDA) provides a way of extracting such information from the geometric structure of the data and identifying multi-way higher-order relationships. Here, we propose an application of harmonic persistent homology, which overcomes the limitation of the ambiguity of the choice of a cycle representing a specific homology class. When applied to multi-omics data, this leads to the discovery of hidden patterns highlighting the relationships between different omic profiles, while allowing for common tasks in multi-omics analyses, such as disease subtyping, and most importantly biomarker identification for similar latent biological pathways that are associated with complex diseases. Our experiments on multiple cancer data show that harmonic persistent homology effectively dissects multi-omics data to identify biomarkers by detecting representative cycles predictive of disease subtypes.
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