Daniele Ramazzotti

Giancarlo Mauri, Marco S. Nobile, Paolo Cazzaniga, 2016, 2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB).

TRONCO: an R package for the inference of cancer progression models from heterogeneous genomic data

Giancarlo Mauri, Giulio Caravagna, Daniele Ramazzotti, 2015 .

Algorithmic methods to infer the evolutionary trajectories in cancer progression

Giancarlo Mauri, Giulio Caravagna, Daniele Ramazzotti, 2015 .

Design of the TRONCO BioConductor Package for TRanslational ONCOlogy

Giancarlo Mauri, Giulio Caravagna, Daniele Ramazzotti, 2016, R J..

Efficient inference of cancer progression models

Giancarlo Mauri, Giulio Caravagna, Bud Mishra, 2014 .

Modeling cumulative biological phenomena with Suppes-Bayes Causal Networks

Giulio Caravagna, Daniele Ramazzotti, Alex Graudenzi, 2016 .

Efficient Simulation of Financial Stress Testing Scenarios with Suppes-Bayes Causal Networks

Bud Mishra, Daniele Ramazzotti, Gelin Gao, 2017, ICCS.

A Model of Selective Advantage for the Efficient Inference of Cancer Clonal Evolution pdf

Daniele Ramazzotti, Daniele Ramazzotti, 2016, ArXiv.

Inferring tree causal models of cancer progression with probability raising

Giancarlo Mauri, Giulio Caravagna, Daniele Ramazzotti, 2014 .

Causal data science for financial stress testing

Bud Mishra, Daniele Ramazzotti, Gelin Gao, 2017, J. Comput. Sci..

SIMLR: a tool for large-scale single-cell analysis by multi-kernel learning

Bo Wang, Junjie Zhu, Daniele Ramazzotti, 2017, bioRxiv.

Withholding aggressive treatments may not accelerate time to death among dying ICU patients pdf

Daniele Ramazzotti, Leo Anthony Celi, David J. Stone, 2018, ArXiv.

On learning the structure of Bayesian Networks and submodular function maximization pdf

Giulio Caravagna, Guido Sanguinetti, Daniele Ramazzotti, 2017, ArXiv.

Inferring Tree Causal Models of Cancer Progression with Probability Raising

Giancarlo Mauri, Giulio Caravagna, Bud Mishra, 2013, bioRxiv.

Multi-objective optimization to explicitly account for model complexity when learning Bayesian Networks pdf

Marco S. Nobile, Paolo Cazzaniga, Daniele Ramazzotti, 2018, ArXiv.

Correction to: Exposing the probabilistic causal structure of discrimination

Francesco Bonchi, Bud Mishra, Daniele Ramazzotti, 2019, International Journal of Data Science and Analytics.

TRONCO: an R package for the inference of cancer progression models from heterogeneous genomic data

Giancarlo Mauri, Giulio Caravagna, Bud Mishra, 2015, bioRxiv.

Implementation of the TRONCO package for TRanslational ONCOlogy

Giancarlo Mauri, Giulio Caravagna, Daniele Ramazzotti, 2015 .

Inferring causal models of cancer progression with a shrinkage estimator and probability raising

Giancarlo Mauri, Giulio Caravagna, Daniele Ramazzotti, 2013 .

Probabilistic Causal Analysis of Social Influence

Francesco Bonchi, Bud Mishra, Daniele Ramazzotti, 2018, CIKM.

cyTRON and cyTRON/JS: two Cytoscape-based applications for the inference of cancer evolution models

Giulio Caravagna, Daniele Ramazzotti, Edoardo Galimberti, 2017, CIBB 2017.

CAPRI: Efficient Inference of Cancer Progression Models from Cross-sectional Data

Giancarlo Mauri, Giulio Caravagna, Daniele Ramazzotti, 2015 .

Design of the TRONCO BioConductor Package for TRanslational ONCOlogy

Giancarlo Mauri, Giulio Caravagna, Daniele Ramazzotti, 2015 .

Learning the Probabilistic Structure of Cumulative Phenomena with Suppes-Bayes Causal Networks

Marco S. Nobile, Daniele Ramazzotti, Marco Antoniotti, 2017, ICCS.

Efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena

Marco S. Nobile, Daniele Ramazzotti, Marco Antoniotti, 2017, J. Comput. Sci..

CAPRI: Efficient Inference of Cancer Progression Models from Cross-sectional Data

Giancarlo Mauri, Giulio Caravagna, Bud Mishra, 2014, bioRxiv.

cyTRON and cyTRON/JS: Two Cytoscape-Based Applications for the Inference of Cancer Evolution Models

Giulio Caravagna, Daniele Ramazzotti, Luca De Sano, 2019, CIBB.

Inference of Cancer Progression Models with Biological Noise

Giulio Caravagna, Bud Mishra, Daniele Ramazzotti, 2014, ArXiv.

Learning the structure of Bayesian Networks via the bootstrap

Giulio Caravagna, Daniele Ramazzotti, 2017 .

Modeling Cumulative Biological Phenomena with Suppes-Bayes Causal Networks

Daniele Ramazzotti, Marco Antoniotti, Alex Graudenzi, 2016, bioRxiv.

Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning

Junjie Zhu, Daniele Ramazzotti, Serafim Batzoglou, 2016, bioRxiv.

The Influence of Nutrients Diffusion on a Metabolism-driven Model of a Multi-cellular System

Giulio Caravagna, Riccardo Colombo, Daniele Ramazzotti, 2020, Fundam. Informaticae.

Investigating the performance of multi-objective optimization when learning Bayesian Networks

Marco S. Nobile, Paolo Cazzaniga, Daniele Ramazzotti, 2018, Neurocomputing.

PMCE: efficient inference of expressive models of cancer evolution with high prognostic power

Giulio Caravagna, Daniele Ramazzotti, Chiara Damiani, 2021 .

PMCE: efficient inference of expressive models of cancer evolution with high prognostic power

Giulio Caravagna, Daniele Ramazzotti, Chiara Damiani, 2022, Bioinform..

Improved Survival of Cancer Patients Admitted to the Intensive Care Unit between 2002 and 2011 at a U.S. Teaching Hospital

Daniele Ramazzotti, Leo Anthony Celi, L. Celi, 2018, Cancer research and treatment : official journal of Korean Cancer Association.

Algorithmic methods to infer the evolutionary trajectories in cancer progression

Giancarlo Mauri, Giulio Caravagna, Bud Mishra, 2015, Proceedings of the National Academy of Sciences.

Multi-omic tumor data reveal diversity of molecular mechanisms that correlate with survival

Daniele Ramazzotti, Serafim Batzoglou, Arend Sidow, 2018, Nature Communications.

Learning mutational graphs of individual tumor evolution from multi-sample sequencing data

Giulio Caravagna, Daniele Ramazzotti, Luca De Sano, 2017 .

Longitudinal cancer evolution from single cells

Daniele Ramazzotti, Rocco Piazza, Davide Maspero, 2020, bioRxiv.

De novo mutational signature discovery in tumor genomes using SparseSignatures

Robert Tibshirani, Daniele Ramazzotti, Arend Sidow, 2018, bioRxiv.

Detecting repeated cancer evolution from multi-region tumor sequencing data

Giulio Caravagna, Guido Sanguinetti, Daniele Ramazzotti, 2018, Nature Methods.