Enhancing Prognostic Signatures in Glioblastoma with Feature Selection and Regularised Cox Regression
This study underscored the utility of combining machine learning feature selection with survival analysis to enhance prognostic modelling while balancing predictive performance and interpretability.
Authors
- Susana Vinga (ORCID: https://orcid.org/0000-0002-1954-5487)
- Alexandra M. Carvalho (ORCID: https://orcid.org/0000-0001-6607-7711)
- André Veríssimo (ORCID: https://orcid.org/0000-0002-2212-339X)
- Beatriz N. Leitão (ORCID: https://orcid.org/0009-0005-4147-9358)
Institutions
- Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento (PT)
- University of Lisbon (PT)
- Universidade Politécnica de Lisboa (PT)
- Instituto de Telecomunicações (PT)
Publication Details
- Journal
- Genes
- Published
- 2025-04-23
- DOI
- https://doi.org/10.3390/genes16050473
- Citations
- 1
- Primary Topic
- Ferroptosis and cancer prognosis
- Type
- article
- Field-Weighted Citation Impact
- 1.52
Funders
- European Commission
- Eidgenössische Technische Hochschule Zürich
- Instituto de Telecomunicações
- Fundação para a Ciência e a Tecnologia