Integration of Multiple scRNA-Seq Datasets on the Autoencoder Latent Space. - Simone G. Riva; Paolo Cazzaniga; Andrea Tangherloni (BIBM, 2021)

The Impact of Representation on the Optimization of Marker Panels for Single-cell RNA Data. - Andrea Tangherloni; Simone G. Riva; Simone Spolaor; Daniela Besozzi; Marco S. Nobile; Paolo Cazzaniga (CEC, 2021)

Predicting and Characterizing Legal Claims of Hospitals with Computational Intelligence: the Legal and Ethical Implications. - Chiara Gallese; Caro Fuchs; Simone G. Riva; Emanuela Foglia; Fabrizio Schettini; Lucrezia Ferrario; Elena Falletti; Marco S. Nobile (CIBCB, 2022)

A Deep Learning Pipeline for the Automatic cell type Assignment of scRNA-seq Data. - Simone G. Riva; Brynelle Myers; Paolo Cazzaniga; Andrea Tangherloni (CIBCB, 2022)

Multi-objective Optimization for Marker Panel Identification in Single-cell Data. - Andrea Tangherloni; Simone G. Riva; Brynelle Myers; Paolo Cazzaniga (CIBCB, 2022)

SMGen: A Generator of Synthetic Models of Biochemical Reaction Networks. - Simone G. Riva; Paolo Cazzaniga; Marco S. Nobile; Simone Spolaor; Leonardo Rundo; Daniela Besozzi; Andrea Tangherloni (Symmetry, 2022)

SMaSH: a scalable, general marker gene identification framework for single-cell RNA-sequencing. - M. E. Nelson; Simone G. Riva; Ana Cvejic (BMC Bioinform., 2022)

Consensus Clustering Strategy for Cell Type Assignments of scRNA-seq Data. - Simone G. Riva; Brynelle Myers; Paolo Cazzaniga; Francesca M. Buffa; Andrea Tangherloni (CIBCB, 2023)

MAGNETO: Cell type marker panel generator from single-cell transcriptomic data. - Andrea Tangherloni; Simone G. Riva; Brynelle Myers; Francesca M. Buffa; Paolo Cazzaniga (J. Biomed. Informatics, 2023)