Identification of ordinal relations and alternative suborders within high-dimensional molecular data
This approach preserves the inherent ordinal structure by projecting high-dimensional data onto one single dimension while simultaneously decreasing complexity. Additionally, the distinct features of the resulting thresholds allow the prediction of potential alternative paths among the suborders.
Authors
- Hans A. Kestler (ORCID: https://orcid.org/0000-0002-4759-5254)
- Angelika Kestler (ORCID: https://orcid.org/0000-0001-5214-9884)
- Ana Stolnicu (ORCID: https://orcid.org/0000-0003-3936-5851)
- Peter Bellmann (ORCID: https://orcid.org/0000-0003-0182-4469)
Institutions
- Universität Ulm (DE)
- Leibniz Institute on Aging - Fritz Lipmann Institute (FLI) (DE)
- University Hospital Ulm (DE)
Publication Details
- Journal
- Frontiers in Bioinformatics
- Published
- 2025-11-03
- DOI
- https://doi.org/10.3389/fbinf.2025.1665892
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Deutsche Forschungsgemeinschaft
- Bundesministerium für Bildung und Forschung