3 papers
cs.LG2025
Beyond Cox Models: Assessing the Performance of Machine-Learning Methods in Non-Proportional Hazards and Non-Linear Survival Analysis
Ivan Rossi, Flavio Sartori, Cesare Rollo +3
Survival analysis often relies on Cox models, assuming both linearity and proportional hazards (PH). This study evaluates machine and deep learning methods that relax these constra…
q-bio.QM2025
Sparsity is All You Need: Rethinking Biological Pathway-Informed Approaches in Deep Learning
Isabella Caranzano, Corrado Pancotti, Cesare Rollo +4
Biologically-informed neural networks typically leverage pathway annotations to enhance performance in biomedical applications. We hypothesized that the benefits of pathway integra…
q-bio.QM2025
SurvHive: a package to consistently access multiple survival-analysis packages
Giovanni Birolo, Ivan Rossi, Flavio Sartori +3
Survival analysis, a foundational tool for modeling time-to-event data, has seen growing integration with machine learning (ML) approaches to handle the complexities of censored da…