Chiara Damiani

发表

The role of backward reactions in a stochastic model of catalytic reaction networks

Roberto Serra, Marco Villani, Chiara Damiani, 2013, ECAL.

Computational Strategies for a System-Level Understanding of Metabolism

Marco S. Nobile, Riccardo Colombo, Dario Pescini, 2014, Metabolites.

Dynamical regimes in non-ergodic random Boolean networks

Roberto Serra, Andrea Roli, Marco Villani, 2015, Natural Computing.

An ensemble approach to the study of the emergence of metabolic and proliferative disorders via Flux Balance Analysis

Giancarlo Mauri, Riccardo Colombo, Dario Pescini, 2013, WIVACE.

A metabolic core model elucidates how enhanced utilization of glucose and glutamine, with enhanced glutamine-dependent lactate production, promotes cancer cell growth: The WarburQ effect

Giancarlo Mauri, Riccardo Colombo, Hans V. Westerhoff, 2017, PLoS Comput. Biol..

MaREA4Galaxy: Metabolic reaction enrichment analysis and visualization of RNA-seq data within Galaxy

Giancarlo Mauri, Chiara Damiani, Lorenzo Rovida, 2020, Computational and structural biotechnology journal.

Combining denoising of RNA-seq data and flux balance analysis for cluster analysis of single cells

C. Damiani, M. Vanoni, B. Galuzzi, 2022, BMC Bioinformatics.

INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation

L. Alberghina, C. Consolandi, D. Pescini, 2021, bioRxiv.

Systems metabolomics: from metabolomic snapshots to design principles.

Chiara Damiani, Lilia Alberghina, Marco Vanoni, 2020, Current opinion in biotechnology.

Integration of single-cell RNA-seq data into population models to characterize cancer metabolism

Giancarlo Mauri, Hans V. Westerhoff, Chiara Damiani, 2019, PLoS Comput. Biol..

Integration of transcriptomic data and metabolic networks in cancer samples reveals highly significant prognostic power

Giancarlo Mauri, Davide Maspero, Marco Antoniotti, 2018, J. Biomed. Informatics.

Integration of single-cell RNA-seq data into metabolic models to characterize tumour cell populations

Giancarlo Mauri, Davide Maspero, Marzia Di Filippo, 2018, bioRxiv.

MaREA: Metabolic feature extraction, enrichment and visualization of RNAseq data

G. Mauri, C. Damiani, M. Antoniotti, 2018, bioRxiv.

Metabolic enrichment through functional gene rules

G. Mauri, C. Damiani, M. Antoniotti, 2017, 1710.06017.

Unity is strength: Improving the detection of adversarial examples with ensemble approaches

Fabio Stella, C. Damiani, M. Antoniotti, 2021, Neurocomputing.

Omics and Clinical Data Integration

C. Damiani, E. Sacco, M. Vanoni, 2018 .

Tumor heterogeneity: preclinical models, emerging technologies, and future applications

C. Damiani, E. Sacco, M. Vanoni, 2023, Frontiers in Oncology.

Zooming-in on cancer metabolic rewiring with tissue specific constraint-based models

Giancarlo Mauri, Riccardo Colombo, Chiara Damiani, 2016, Comput. Biol. Chem..

An ensemble evolutionary constraint-based approach to understand the emergence of metabolic phenotypes

L. Alberghina, G. Mauri, D. Pescini, 2014, Natural Computing.

Constraint-Based Modeling and Simulation of Cell Populations

Giancarlo Mauri, Riccardo Colombo, Dario Pescini, 2016, WIVACE.

Divergent in vitro/in vivo responses to drug treatments of highly aggressive NIH-Ras cancer cells: a PET imaging and metabolomics-mass-spectrometry study

L. Alberghina, A. Luini, M. Gilardi, 2016, Oncotarget.

CABeRNET: a Cytoscape app for augmented Boolean models of gene regulatory NETworks

Giancarlo Mauri, Giulio Caravagna, Marco Antoniotti, 2015, BMC Bioinformatics.

Investigating the Role of Network Topology and Dynamical Regimes on the Dynamics of a Cell Differentiation Model

Roberto Serra, Marco Villani, Marco Antoniotti, 2014, WIVACE.

Explorer CABeRNET : a Cytoscape app for augmented Boolean models of gene regulatory NETworks

G. Mauri, C. Damiani, M. Antoniotti, 2016 .

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

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

Parameter sensitivity analysis of stochastic models: Application to catalytic reaction networks

Paola Lecca, Chiara Damiani, Alessandro Filisetti, 2013, Comput. Biol. Chem..

Coupling constrained-based flux sampling and clustering to tackle cancer metabolic heterogeneity

L. Alberghina, C. Damiani, B. Galuzzi, 2023, 2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP).