Daniele Ramazzotti

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

Early detection and improved genomic surveillance of SARS-CoV-2 variants from deep sequencing data

M. Antoniotti, A. Graudenzi, Davide Maspero, 2021, iScience.

Mutational signatures and heterogeneous host response revealed via large-scale characterization of SARS-CoV-2 genomic diversity

D. Ramazzotti, A. Graudenzi, F. Angaroni, 2020, bioRxiv.

SARS-COV-2 ALLOWS A HIGH-RESOLUTION CHARACTERIZATION OF VIRAL EVOLUTION AND REVEALS FUNCTIONALLY CONVERGENT VARIANTS

D. Ramazzotti, M. Antoniotti, A. Graudenzi, 2020 .

VERSO: A comprehensive framework for the inference of robust phylogenies and the quantification of intra-host genomic diversity of viral samples

M. Antoniotti, A. Graudenzi, Davide Maspero, 2020, bioRxiv.

VirMutSig: Discovery and assignment of viral mutational signatures from sequencing data

A. Graudenzi, D. Porro, Davide Maspero, 2021, STAR Protocols.

Large-scale analysis of SARS-CoV-2 synonymous mutations reveals the adaptation to the human codon usage during the virus evolution

M. Antoniotti, A. Graudenzi, Mario Mauri, 2022, bioRxiv.

Large-scale analysis of synonymous viral variants reveals global adaptation of the SARS-CoV-2 to the human codon usage

M. Antoniotti, A. Graudenzi, Mario Mauri, 2021 .

Efficient inference of cancer progression models

Giancarlo Mauri, Giulio Caravagna, Bud Mishra, 2014 .

A Computational Framework To Infer The Order Of Accumulating Mutations In Individual Tumors

Luca De Sano, D. Ramazzotti, M. Antoniotti, 2017 .

LACE 2.0: an interactive R tool for the inference and visualization of longitudinal cancer evolution

C. Damiani, M. Antoniotti, A. Graudenzi, 2023, BMC Bioinformatics.

LACE: Inference of cancer evolution models from longitudinal single-cell sequencing data

M. Antoniotti, A. Graudenzi, I. Castiglioni, 2021, J. Comput. Sci..

Longitudinal cancer evolution from single cells

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

VERSO: A comprehensive framework for the inference of robust phylogenies and the quantification of intra-host genomic diversity of viral samples

D. Ramazzotti, M. Antoniotti, A. Graudenzi, 2020, bioRxiv.

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.

Assessment of the evolutionary consequence of putative driver mutations in colorectal cancer with spatial multiomic data

Chris P. Barnes, George D. Cresswell, L. Magnani, 2021, bioRxiv.

Integrated Genomic, Functional, and Prognostic Characterization of Atypical Chronic Myeloid Leukemia

M. Fumagalli, M. Merli, S. Redaelli, 2020, HemaSphere.

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

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

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

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

DNA Damage Response (DDR) Is Associated With Treatment-free Remission in Chronic Myeloid Leukemia Patients

Mario Mauri, C. Gambacorti-Passerini, R. Piazza, 2023, HemaSphere.

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..

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

Marco S. Nobile, D. Ramazzotti, M. Antoniotti, 2017, ArXiv.

Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients

L. Celi, D. Ramazzotti, Matteo Bonvini, 2019, npj Digital Medicine.

Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data

Giulio Caravagna, Alex Graudenzi, Marco Antoniotti, 2017, BMC Bioinformatics.

Probabilistic Causal Analysis of Social Influence

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

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

Giulio Caravagna, Daniele Ramazzotti, Marco Antoniotti, 2017, bioRxiv.

Causal data science for financial stress testing

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

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

B. Mishra, D. Ramazzotti, Gelin Gao, 2017, ICCS.

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

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

Inferring Tree Causal Models of Cancer Progression with Probability Raising

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

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

Giancarlo Mauri, Giulio Caravagna, Daniele Ramazzotti, 2013 .

Causal Data Science for Financial Stress Testing 1

B. Mishra, D. Ramazzotti, Gelin Gao, 2018 .

Improved survival of cancer patients admitted to the ICU between 2002 and 2011 at a U.S. teaching hospital

L. Celi, D. Ramazzotti, Jinghui Dong, 2018, 1808.02766.

Characterization of cancer subtypes associated with clinical outcomes by multi-omics integrative clustering

A. Graudenzi, R. Piazza, L. Mologni, 2023, Comput. Biol. Medicine.

Learning the Structure of Bayesian Networks: A Quantitative Assessment of the Effect of Different Algorithmic Schemes

Daniele Ramazzotti, Stefano Beretta, Mauro Castelli, 2017, Complex..

A quantitative assessment of the effect of different algorithmic schemes to the task of learning the structure of Bayesian Networks pdf

Daniele Ramazzotti, Stefano Beretta, Mauro Castelli, 2017, ArXiv.

Evolutionary signatures of human cancers revealed via genomic analysis of over 35,000 patients

Luca De Sano, M. Antoniotti, G. Caravagna, 2023, bioRxiv.

Effects of blocking CD24 and CD47 ‘don't eat me’ signals in combination with rituximab in mantle‐cell lymphoma and chronic lymphocytic leukaemia

C. Voena, R. Chiarle, Mario Mauri, 2023, Journal of cellular and molecular medicine.

A Model of Colonic Crypts using SBML Spatial

Daniele Ramazzotti, Marco Antoniotti, Carlo Maj, 2013, WIVACE.

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

G. Mauri, B. Mishra, M. Antoniotti, 2014, bioRxiv.

LACE 2.0: an interactive R tool for the inference and visualization of longitudinal cancer evolution

C. Damiani, M. Antoniotti, Gianluca Ascolani, 2023, BMC Bioinformatics.

Combining Bayesian Approaches and Evolutionary Techniques for the Inference of Breast Cancer Networks

Ivan Merelli, Daniele Ramazzotti, Stefano Beretta, 2017, bioRxiv.

Oncoscore, a Novel, Internet-Based Tool to Assess the Oncogenic Potential of Genes Can Differentiate Between CP-CML and BC-CML Associated Genes, and Between CP-CML Patients with Good and Bad Prognosis

Luca De Sano, Nitesh D. Sharma, C. Gambacorti-Passerini, 2016 .

Tumor evolution metrics predict recurrence beyond 10 years in locally advanced prostate cancer.

George D. Cresswell, Z. Kote-Jarai, V. Stavrinides, 2024, Nature cancer.

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

G. Caravagna, Daniele Ramazzotti, Fabrizio Angaroni, 2014, bioRxiv.