Integrating trajectory inference and self-explainable predictive models to explore cell state transitions in breast cancer at single-cell resolution
Unlike black-box models, our framework provides transparent, rule-based insights into transcriptional reprogramming processes underlying tumor progression. The resulting dataset, together with an accessible and transparent analytical pipeline, represents a valuable resource for the breast cancer research community and establishes a foundation for future studies aimed at refining molecular classification and advancing precision therapy development.
Authors
- Rosamaria Lappano (ORCID: https://orcid.org/0000-0002-9374-9701)
- Marianna Talia (ORCID: https://orcid.org/0000-0002-3440-5618)
- Domenica Scordamaglia (ORCID: https://orcid.org/0000-0001-8384-4230)
- Anna Miglietta
- Marcello Maggiolini (ORCID: https://orcid.org/0000-0002-7485-854X)
- S. Giordano (ORCID: https://orcid.org/0009-0000-1484-4579)
- Eugenio Cesario
- Santina Capalbo
- Vanessa Verrina
Institutions
- Università degli Studi di Enna Kore (IT)
- Magna Graecia University (IT)
- Nuovo Ospedale San Giovanni di Dio (IT)
- Ospedale Annunziata di Cosenza (IT)
- University of Calabria (IT)
Publication Details
- Journal
- Frontiers in Bioinformatics
- Published
- 2026-03-04
- DOI
- https://doi.org/10.3389/fbinf.2026.1672671
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 0.00