4 citations · 8 across the 7 of their papers we have counts for
9 papers
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…
Human mutation field reveals an equilibrium-like structure with irreversible circulation
Isabella Caranzano, Daniel Maria Busiello, Stefano Priorelli +2
The evolution of DNA sequences can be viewed as stochastic dynamics on a high-dimensional discrete space, but it is unclear when empirical transition biases reduce to an effective…
Neural Diffusion Processes for Physically Interpretable Survival Prediction
Alessio Cristofoletto, Cesare Rollo, Giovanni Birolo +1
We introduce DeepFHT, a survival-analysis framework that couples deep neural networks with first hitting time (FHT) distributions from stochastic process theory. Time to event is r…
The Advantage of Fine-Grained Training
Davide Pirovano, Federico Milanesio, Michele Caselle +2
In classification problems, models are trained to predict a class label based on the input data features. However, class labels are organized hierarchically in many datasets. While…
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…
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…
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…
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…
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…