7 citations · 7 across the 4 of their papers we have counts for
6 papers
Prior-informed conditional Gaussian graphical models: an application to protein interaction network reconstruction
Alessia Mapelli, Michela Carlotta Massi, Gianmauro Cuccuru +2
Protein-protein interaction (PPI) networks, estimated from high-throughput omics data, foster biomarker discovery and precision medicine. Gaussian graphical models (GGMs) offer a p…
SNPgen: Phenotype-Supervised Genotype Representation and Synthetic Data Generation via Latent Diffusion
Andrea Lampis, Michela Carlotta Massi, Nicola Pirastu +3
Polygenic risk scores and other genomic analyses require large individual-level genotype datasets, yet strict data access restrictions impede sharing. Synthetic genotype generation…
Learning Signal Representations for EEG Cross-Subject Channel Selection and Trial Classification
Michela C. Massi, Francesca Ieva
EEG technology finds applications in several domains. Currently, most EEG systems require subjects to wear several electrodes on the scalp to be effective. However, several channel…
A Deep Variational Approach to Clustering Survival Data
Laura Manduchi, Ričards Marcinkevičs, Michela C. Massi +9
In this work, we study the problem of clustering survival data a challenging and so far under-explored task. We introduce a novel semi-supervised probabilistic approach to clus…
Feature Selection for Imbalanced Data with Deep Sparse Autoencoders Ensemble
Michela C. Massi, Francesca Ieva, Francesca Gasperoni +1
Class imbalance is a common issue in many domain applications of learning algorithms. Oftentimes, in the same domains it is much more relevant to correctly classify and profile min…
Learning High-Order Interactions via Targeted Pattern Search
Michela C. Massi, Nicola R. Franco, Francesca Ieva +3
Logistic Regression (LR) is a widely used statistical method in empirical binary classification studies. However, real-life scenarios oftentimes share complexities that prevent fro…