Publications of Irene Balelli

1. Francesco Cremonesi, Marc Vesin, Sergen Cansiz, Yannick Bouillard, Irene Balelli, Lucia Innocenti, Riccardo Taiello, Santiago Silva, Samy-Safwan Ayed, Melek Önen, Fanny Orlhac, Christophe Nioche, Bastien Houis, Romain Modzelewski, Nathan Lapel, Renaud Schiappa, Olivier Humbert, and Marco Lorenzi. Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications. In Federated Learning Systems, volume 832 of Studies in Computational Intelligence, pages 19-41. Springer Nature Switzerland, April 2025. Keyword(s): Machine learning, Biomedical Application, Healthcare, Federated Learning Framework. [bibtex-entry]



2. Irene Balelli, Safaa Al-Ali, Elise Dumas, and Judith Abécassis. Causality: fundamental principles and tools. In Trustworthy AI in Medical Imaging, number Chapitre 14, pages 297-314. November 2024. Keyword(s): Causal learning, Discovery, Inference, Bias, Counfounder, Counterfactual queries. [bibtex-entry]



3. Quentin Clairon, Chloé Pasin, Irene Balelli, Rodolphe Thiébaut, and Mélanie Prague. Parameter estimation in nonlinear mixed effect models based on ordinary differential equations: An optimal control approach. Computational Statistics, September 2023. Keyword(s): Dynamic population models, Ordinary differential equations, Optimal control theory, Mechanistic models, Nonlinear mixed effects models, Clinical trial analysis. [bibtex-entry]



4. Marco Lorenzi, Marie Deprez, Irene Balelli, Ana L Aguila, and Andre Altmann. Integration of Multimodal Data. In Olivier Colliot, editor, Machine Learning for Brain Disorders, volume NM197 of Neuromethods, pages 573 - 597. Springer, 2023. Keyword(s): Multivariate analysis, Latent variable models, Multimodal imaging, -Omics, Imaginggenetics, Partial least squares, Canonical correlation analysis, Variational autoencoders, Sparsity, Interpretability. [bibtex-entry]



5. Irene Balelli, Santiago Silva, and Marco Lorenzi. A Differentially Private Probabilistic Framework for Modeling the Variability Across Federated Datasets of Heterogeneous Multi-View Observations. Journal of Machine Learning for Biomedical Imaging, April 2022. [bibtex-entry]

1. Safaa Al-Ali, Maria T Mora, Maxime Sermesant, Beatriz Trénor, and Irene Balelli. Assessing Ionic Current Blockades and Electromechanical Biomarkers' Interrelations Through a Novel Multi-Channel Causal Variational Autoencoder. In IEEE, editor, Computing in Cardiology 2024, volume 51, Karlsruhe (Germany), Germany, September 2024. Keyword(s): Multi-channel, Variational auto-encoder, Torsade de pointes, Risk assesmant, Ion channel block, Electromechanical characteristics. [bibtex-entry]



2. Yves Coudière, Michael Leguèbe, Irene Balelli, Alessia Baretta, Guilhem Fauré, and Delphine Feuerstein. A Computer Model for In Silico Trials on Pacemaker Energy Efficiency. In Computing in Cardiology 2024, Karlsruhe, Germany, September 2024. [bibtex-entry]



3. Safaa Al-Ali, Jordi Llopis-Lorente, Maria Teresa Mora, Maxime Sermesant, Beatriz Trénor, and Irene Balelli. A causal discovery approach for streamline ion channels selection to improve drug-induced TdP risk assessment. In IEEE Xplore, 2023 Computing in Cardiology (CinC), Atlanta (GA), United States, October 2023. Keyword(s): Causal discovery, Drug safety, Ions channel, TdP risk. [bibtex-entry]



4. Irene Balelli, Santiago Silva, and Marco Lorenzi. A Probabilistic Framework for Modeling the Variability Across Federated Datasets of Heterogeneous Multi-View Observations. In International Conference on Information Processing in Medical Imaging, Information processing in medical imaging: proceedings of the 27th International Conference, IPMI 2021, Bornholm, Denmark, June 2021. Keyword(s): Federated Learning, Hierarchical Generative Model, Heterogeneity. [bibtex-entry]

1. Safaa Al-Ali and Irene Balelli. Multi-Channel Causal Variational Autoencoder. Note: Working paper or preprint, August 2024. Keyword(s): Multi-channel, Causal discovery, Variational Autoencoder. [bibtex-entry]



2. Irene Balelli, Aude Sportisse, Francesco Cremonesi, Pierre-Alexandre Mattei, and Marco Lorenzi. Fed-MIWAE: Federated Imputation of Incomplete Data via Deep Generative Models. Note: Working paper or preprint, 2023. Keyword(s): Missing data, Federated learning, Federated pre-processing, Variational autoencoders, Deep Learning. [bibtex-entry]

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