BioData Min. 2026 May 15. doi: 10.1186/s13040-026-00559-9. Online ahead of print.
ABSTRACT
BACKGROUND: Survival analysis in high-dimensional (HD) and multi-block (MB) settings, such as omic and multi-omic studies, poses major methodological challenges due to multicollinearity, low events-per-variable ratios, and limited model interpretability. While Cox Proportional Hazards models remain widely used, their applicability is restricted in HD scenarios. Machine learning approaches can handle such complexity but often lack interpretability. Survival models based on Partial Least Squares (PLS) offer an attractive compromise, yet existing implementations provide limited support for model optimization, evaluation, and interpretation.
RESULTS: To overcome these limitations, we introduce Coxmos, a CRAN R package that integrates adapted Cox regression models with HD-adapted variable selection, and optimized PLS-based approaches tailored for HD and MB data. Coxmos also provides validation, comparison, interpretation, and visualization tools. Benchmarking on a diverse set of clinical, single-omic, and multi-omic cancer datasets – including breast (BRCA), head and neck (HNSC), and ovarian (HGSOC) cancer – demonstrated that Coxmos outperformed other state-of-the-art machine learning methods, while enhancing interpretability. We illustrated the package’s functionalities through two case studies (HGSOC and HNSC), showing how Coxmos facilitates model selection, validation, and biological interpretation in a complex HD and MB survival analysis.
CONCLUSIONS: Coxmos provides a robust, flexible, and interpretable solution for survival analysis in HD and MB data. By combining optimized PLS-based methodologies with rigorous evaluation and rich visualization tools, Coxmos enables reliable survival prediction and facilitates the identification of relevant risk and protective factors in complex biomedical studies. The package fills an important methodological gap and represents a valuable resource for reproducible and interpretable survival modeling in the big data scenario.
AVAILABILITY: Coxmos is available at CRAN repository: https://cran.r-project.org/web/packages/Coxmos.
PMID:42141472 | DOI:10.1186/s13040-026-00559-9
