1 citations · 2 across the 9 of their papers we have counts for
9 papers
The Role of Uncertainty in Assessing the Fairness of Machine Learning Models
Francesca Panero, Ernst C. Wit, Marco Scutari
Machine learning models are widely used in clinical applications, social media, law enforcement and critical infrastructure. Verifying whether their outputs are biased against disa…
FPLIER: Federated Pathway-Level Information Extractor
Daniele Malpetti, Christian Berchtold, Francesco Gualdi +3
In transcriptomics, gene-set-aware factorization methods such as the Pathway Level Information Extractor (PLIER) are most effective when trained on large, heterogeneous expression…
DAG Learning from Zero-Inflated Count Data Using Continuous Optimization
Noriaki Sato, Marco Scutari, Shuichi Kawano +2
We address network structure learning from zero-inflated count data by casting each node as a zero-inflated generalized linear model and optimizing a smooth, score-based objective…
Causal Discovery on Higher-Order Interactions
Alessio Zanga, Marco Scutari, Fabio Stella
Causal discovery combines data with knowledge provided by experts to learn the DAG representing the causal relationships between a given set of variables. When data are scarce, bag…
Practical Causal Evaluation Metrics for Biological Networks
Noriaki Sato, Marco Scutari, Shuichi Kawano +2
Estimating causal networks from biological data is a critical step in systems biology. When evaluating the inferred network, assessing the networks based on their intervention effe…
Causal Networks of Infodemiological Data: Modelling Dermatitis
Marco Scutari, Samir Salah, Delphine Kerob +1
Environmental and mental conditions are known risk factors for dermatitis and symptoms of skin inflammation, but their interplay is difficult to quantify; epidemiological studies r…
Technical and Legal Aspects of Federated Learning in Bioinformatics: Applications, Challenges and Opportunities
Daniele Malpetti, Marco Scutari, Francesco Gualdi +6
Federated learning leverages data across institutions to improve clinical discovery while complying with data-sharing restrictions and protecting patient privacy. This paper provid…
Inferring Skin-Brain-Skin Connections from Infodemiology Data using Dynamic Bayesian Networks
Marco Scutari, Delphine Kerob, Samir Salah
The relationship between skin diseases and mental illnesses has been extensively studied using cross-sectional epidemiological data. Typically, such data can only measure associati…
Entropy and the Kullback-Leibler Divergence for Bayesian Networks: Computational Complexity and Efficient Implementation
Marco Scutari
Bayesian networks (BNs) are a foundational model in machine learning and causal inference. Their graphical structure can handle high-dimensional problems, divide them into a sparse…