most citedBeyond Cox Models: Assessing the Performance of Machine-Learning Methods in Non-Proportional Hazards and Non-Linear Survival Analysis

4 citations · 8 across the 5 of their papers we have counts for

collaborators

6 papers

q-bio.QM2026

Predicting Mutational Signature Exposures from H&E Whole Slide Images: A Pan-Cancer Feasibility Study

Flavio Sartori, Cesare Rollo, Isabella Caranzano +3

Mutational signatures reveal cancer-driving processes with clinical relevance: MMR-deficient tumors respond better to immunotherapy, HRD tumors are sensitive to PARP inhibitors, an…

q-bio.QM2025★ 1 cited

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

JanusDDG: A Thermodynamics-Compliant Model for Sequence-Based Protein Stability via Two-Fronts Multi-Head Attention

Guido Barducci, Ivan Rossi, Francesco Codicè +6

Understanding how residue variations affect protein stability is crucial for designing functional proteins and deciphering the molecular mechanisms underlying disease-related mutat…

q-bio.QM2025★ 2 cited

Mass Balance Approximation of Unfolding Improves Potential-Like Methods for Protein Stability Predictions

Ivan Rossi, Guido Barducci, Tiziana Sanavia +3

The prediction of protein stability changes following single-point mutations plays a pivotal role in computational biology, particularly in areas like drug discovery, enzyme reengi…

cs.LG2025★ 4 cited

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★ 1 cited

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…