6 papers
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…
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…
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…