A Model of Colonic Crypts using SBML Spatial. - Daniele Ramazzotti; Carlo Maj; Marco Antoniotti (WIVACE, 2013)

Inference of Cancer Progression Models with Biological Noise. - Ilya Korsunsky; Daniele Ramazzotti; Giulio Caravagna; Bud Mishra (CoRR, 2014)

CAPRI: efficient inference of cancer progression models from cross-sectional data. - Daniele Ramazzotti; Giulio Caravagna; Loes Olde Loohuis; Alex Graudenzi; Ilya Korsunsky; Giancarlo Mauri; Marco Antoniotti; Bud Mishra (Bioinform., 2015)

Parallel implementation of efficient search schemes for the inference of cancer progression models. - Daniele Ramazzotti; Marco S. Nobile; Paolo Cazzaniga; Giancarlo Mauri; Marco Antoniotti (CIBCB, 2016)

Combining Bayesian Approaches and Evolutionary Techniques for the Inference of Breast Cancer Networks. - Stefano Beretta 001; Mauro Castelli; Ivo Gonçalves; Ivan Merelli; Daniele Ramazzotti (IJCCI (ECTA), 2016)

TRONCO: an R package for the inference of cancer progression models from heterogeneous genomic data. - Luca De Sano; Giulio Caravagna; Daniele Ramazzotti; Alex Graudenzi; Giancarlo Mauri; Bud Mishra; Marco Antoniotti (Bioinform., 2016)

A Model of Selective Advantage for the Efficient Inference of Cancer Clonal Evolution. - Daniele Ramazzotti (CoRR, 2016)

Modeling cumulative biological phenomena with Suppes-Bayes causal networks. - Daniele Ramazzotti; Alex Graudenzi; Marco Antoniotti (CoRR, 2016)

Design of the TRONCO BioConductor Package for TRanslational ONCOlogy. - Marco Antoniotti; Giulio Caravagna; Luca De Sano; Alex Graudenzi; Giancarlo Mauri; Bud Mishra; Daniele Ramazzotti (R J., 2016)

Efficient Simulation of Financial Stress Testing Scenarios with Suppes-Bayes Causal Networks. - Gelin Gao; Bud Mishra; Daniele Ramazzotti (ICCS, 2017)

A quantitative assessment of the effect of different algorithmic schemes to the task of learning the structure of Bayesian Networks. - Stefano Beretta 001; Mauro Castelli; Ivo Gonçalves; Daniele Ramazzotti (CoRR, 2017)

On learning the structure of Bayesian Networks and submodular function maximization. - Giulio Caravagna; Daniele Ramazzotti; Guido Sanguinetti (CoRR, 2017)

Learning the Probabilistic Structure of Cumulative Phenomena with Suppes-Bayes Causal Networks. - Daniele Ramazzotti; Marco S. Nobile; Marco Antoniotti; Alex Graudenzi (CoRR, 2017)

SIMLR: a tool for large-scale single-cell analysis by multi-kernel learning. - Bo Wang 044; Daniele Ramazzotti; Luca De Sano; Junjie Zhu; Emma Pierson; Serafim Batzoglou (CoRR, 2017)

Learning mutational graphs of individual tumor evolution from multi-sample sequencing data. - Daniele Ramazzotti; Alex Graudenzi; Luca De Sano; Marco Antoniotti; Giulio Caravagna (CoRR, 2017)

Exposing the probabilistic causal structure of discrimination. - Francesco Bonchi; Sara Hajian; Bud Mishra; Daniele Ramazzotti (Int. J. Data Sci. Anal., 2017)

Probabilistic Causal Analysis of Social Influence. - Francesco Bonchi; Francesco Gullo; Bud Mishra; Daniele Ramazzotti (CIKM, 2018)

Structural Learning of Probabilistic Graphical Models of Cumulative Phenomena. - Daniele Ramazzotti; Marco S. Nobile; Marco Antoniotti; Alex Graudenzi (ICCS (1), 2018)

Learning the Structure of Bayesian Networks: A Quantitative Assessment of the Effect of Different Algorithmic Schemes. - Stefano Beretta 001; Mauro Castelli; Ivo Gonçalves; Roberto Henriques; Daniele Ramazzotti (Complex., 2018)

Multi-objective optimization to explicitly account for model complexity when learning Bayesian Networks. - Paolo Cazzaniga; Marco S. Nobile; Daniele Ramazzotti (CoRR, 2018)

Withholding aggressive treatments may not accelerate time to death among dying ICU patients. - Daniele Ramazzotti; Peter Clardy; Leo Anthony Celi; David J. Stone; Robert S. Rudin (CoRR, 2018)

Causal data science for financial stress testing. - Gelin Gao; Bud Mishra; Daniele Ramazzotti (J. Comput. Sci., 2018)

cyTRON and cyTRON/JS: Two Cytoscape-Based Applications for the Inference of Cancer Evolution Models. - Lucrezia Patruno; Edoardo Galimberti; Daniele Ramazzotti; Giulio Caravagna; Luca De Sano; Marco Antoniotti; Alex Graudenzi (CIBB, 2019)

Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data. - Daniele Ramazzotti; Alex Graudenzi; Luca De Sano; Marco Antoniotti; Giulio Caravagna (BMC Bioinform., 2019)

Efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena. - Daniele Ramazzotti; Marco S. Nobile; Marco Antoniotti; Alex Graudenzi (J. Comput. Sci., 2019)

Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients. - Antonin Dauvin; Carolina Donado; Patrik Bachtiger; Ke-Chun Huang; Christopher Martin Sauer; Daniele Ramazzotti; Matteo Bonvini; Leo Anthony Celi; Molly J. Douglas (npj Digit. Medicine, 2019)

The Influence of Nutrients Diffusion on a Metabolism-driven Model of a Multi-cellular System. - Davide Maspero; Chiara Damiani; Marco Antoniotti; Alex Graudenzi; Marzia Di Filippo; Marco Vanoni; Giulio Caravagna; Riccardo Colombo; Daniele Ramazzotti; Dario Pescini (Fundam. Informaticae, 2020)

Correction to: Exposing the probabilistic causal structure of discrimination. - Francesco Bonchi; Sara Hajian; Bud Mishra; Daniele Ramazzotti (Int. J. Data Sci. Anal., 2020)

Learning the structure of Bayesian Networks via the bootstrap. - Giulio Caravagna; Daniele Ramazzotti (Neurocomputing, 2021)

Investigating the performance of multi-objective optimization when learning Bayesian Networks. - Marco S. Nobile; Paolo Cazzaniga; Daniele Ramazzotti (Neurocomputing, 2021)

VERSO: A comprehensive framework for the inference of robust phylogenies and the quantification of intra-host genomic diversity of viral samples. - Daniele Ramazzotti; Fabrizio Angaroni; Davide Maspero; Carlo Gambacorti Passerini; Marco Antoniotti; Alex Graudenzi; Rocco Piazza (Patterns, 2021)

De novo mutational signature discovery in tumor genomes using SparseSignatures. - Avantika Lal; Keli Liu; Robert Tibshirani; Arend Sidow; Daniele Ramazzotti (PLoS Comput. Biol., 2021)

Exploring the Solution Space of Cancer Evolution Inference Frameworks for Single-Cell Sequencing Data. - Davide Maspero; Fabrizio Angaroni; Lucrezia Patruno; Daniele Ramazzotti; David Posada; Alex Graudenzi (WIVACE, 2022)

PMCE: efficient inference of expressive models of cancer evolution with high prognostic power. - Fabrizio Angaroni; Kevin Chen; Chiara Damiani; Giulio Caravagna; Alex Graudenzi; Daniele Ramazzotti (Bioinform., 2022)

LACE: Inference of cancer evolution models from longitudinal single-cell sequencing data. - Daniele Ramazzotti; Fabrizio Angaroni; Davide Maspero; Gianluca Ascolani; Isabella Castiglioni; Rocco Piazza; Marco Antoniotti; Alex Graudenzi (J. Comput. Sci., 2022)

Characterization of cancer subtypes associated with clinical outcomes by multi-omics integrative clustering. - Valentina Crippa; Federica Malighetti; Matteo Villa; Alex Graudenzi; Rocco Piazza; Luca Mologni; Daniele Ramazzotti (Comput. Biol. Medicine, 2023)

LACE 2.0: an interactive R tool for the inference and visualization of longitudinal cancer evolution. - Gianluca Ascolani; Fabrizio Angaroni; Davide Maspero; Francesco Craighero; Narra Lakshmi Sai Bhavesh; Rocco Piazza; Chiara Damiani; Daniele Ramazzotti; Marco Antoniotti; Alex Graudenzi (BMC Bioinform., 2023)

Control-FREEC viewer: a tool for the visualization and exploration of copy number variation data. - Valentina Crippa; Emanuela Fina; Daniele Ramazzotti; Rocco Piazza (BMC Bioinform., 2024)