3D CT-Inclusive Deep-Learning Model to Predict Mortality, ICU Admittance, and Intubation in COVID-19 Patients - Alberto Di Napoli; Emanuela Tagliente; Luca Pasquini; Enrica Cipriano; Filomena Pietrantonio; Piermaria Ortis; Simona Curti; Alessandro Boellis; Teseo Stefanini; Antonio Bernardini; Chiara Angeletti; Sofia Chiatamone Ranieri; et al. (J. Digit. Imaging, 2023)

Deep reinforcement learning for fMRI prediction of Autism Spectrum Disorder - Joseph N. Stember; Danielle M. Stember; Luca Pasquini; Mehrnaz Jenabi; Andrei I. Holodny; Hrithwik Shalu (CoRR, 2022)

Deep neuroevolution to predict primary brain tumor grade from functional MRI adjacency matrices - Joseph N. Stember; Mehrnaz Jenabi; Luca Pasquini; Kyung K. Peck; Andrei I. Holodny; Hrithwik Shalu (CoRR, 2022)

Agile workflow for interactive analysis of mass cytometry data - Julia Casado; Oskari Lehtonen; Ville Rantanen; Katja Kaipio; Luca Pasquini; Antti Häkkinen; Elenora Petrucci; Johanna Hynninen; Sakari Hietanen; Olli Carpén; Mauro Biffoni; Anniina Färkkilä; et al. (Bioinform., 2021)

Comparison of Machine Learning Classifiers to Predict Patient Survival and Genetics of GBM: Towards a Standardized Model for Clinical Implementation - Luca Pasquini; Antonio Napolitano; Martina Lucignani; Emanuela Tagliente; Francesco Dellepiane; Maria Camilla Rossi-Espagnet; Matteo Ritrovato; Antonello Vidiri; Veronica Villani; Giulio Ranazzi; Antonella Stoppacciaro; Andrea Romano; et al. (CoRR, 2021)

qSNE: quadratic rate t-SNE optimizer with automatic parameter tuning for large datasets - Antti Häkkinen; Juha Koiranen; Julia Casado; Katja Kaipio; Oskari Lehtonen; Eleonora Petrucci; Johanna Hynninen; Sakari Hietanen; Olli Carpén; Luca Pasquini; Mauro Biffoni; Rainer Lehtonen; et al. (Bioinform., 2020)