Investigating Associations Between Prognostic Factors in Gliomas: Unsupervised Multiple Correspondence Analysis
MCA is a valuable tool for understanding the complex interdependence of prognostic markers in gliomas. MCA facilitates the exploration of large-scale datasets and enhances the identification of significant associations.
Authors
- Pedro Luiz Porfírio Xavier (ORCID: https://orcid.org/0000-0002-2435-9822)
- Heidge Fukumasu (ORCID: https://orcid.org/0000-0002-3265-5090)
- Tathiane M. Malta (ORCID: https://orcid.org/0000-0003-1129-5791)
- Maria Eduarda Goes Job (ORCID: https://orcid.org/0009-0007-3531-7365)
Institutions
- Universidade de Ribeirão Preto (BR)
- Universidade de São Paulo (BR)
- Clinics Hospital of Ribeirão Preto (BR)
Publication Details
- Journal
- JMIR Bioinformatics and Biotechnology
- Published
- 2025-02-04
- DOI
- https://doi.org/10.2196/65645
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00