Publications

2023

2023 Scalable Integration of Multiomic Single Cell Data Using Generative Adversarial Networks Giansanti, V., Giannese, F., Botrugno, O., Gandolfi, G., Balestrieri, C., Antoniotti, M., et al. (2023). Scalable Integration of Multiomic Single Cell Data Using Generative Adversarial Networks [Altro] [10.1101/2023.06.26.546547].2023 A Bayesian method to infer copy number clones from single-cell RNA and ATAC sequencing Patruno, L., Milite, S., Bergamin, R., Calonaci, N., D’Onofrio, A., Anselmi, F., et al. (2023). A Bayesian method to infer copy number clones from single-cell RNA and ATAC sequencing. PLOS COMPUTATIONAL BIOLOGY, 19(11), 1-19 [10.1371/journal.pcbi.1011557].2023 Coupling constrained-based flux sampling and clustering to tackle cancer metabolic heterogeneity Galuzzi, B., Izzo, S., Giampaolo, F., Cuomo, S., Vanoni, M., Alberghina, L., et al. (2023). Coupling constrained-based flux sampling and clustering to tackle cancer metabolic heterogeneity. In 2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) (pp.185-192). IEEE [10.1109/PDP59025.2023.00037].2023 Evolutionary signatures of human cancers revealed via genomic analysis of over 35,000 patients Fontana, D., Crespiatico, I., Crippa, V., Malighetti, F., Villa, M., Angaroni, F., et al. (2023). Evolutionary signatures of human cancers revealed via genomic analysis of over 35,000 patients. NATURE COMMUNICATIONS, 14(1) [10.1038/s41467-023-41670-3].2023 Editorial: Network bioscience Volume II Antoniotti, M., Mishra, B., Pellegrini, M. (2023). Editorial: Network bioscience Volume II. FRONTIERS IN GENETICS, 14 [10.3389/fgene.2023.1256025].2023 Unity is strength: Improving the detection of adversarial examples with ensemble approaches Craighero, F., Angaroni, F., Stella, F., Damiani, C., Antoniotti, M., Graudenzi, A. (2023). Unity is strength: Improving the detection of adversarial examples with ensemble approaches. NEUROCOMPUTING, 554(14 October 2023) [10.1016/j.neucom.2023.126576].2023 Characterization of cancer subtypes associated with clinical outcomes by multi-omics integrative clustering Crippa, V., Malighetti, F., Villa, M., Graudenzi, A., Piazza, R., Mologni, L., et al. (2023). Characterization of cancer subtypes associated with clinical outcomes by multi-omics integrative clustering. COMPUTERS IN BIOLOGY AND MEDICINE, 162(August 2023) [10.1016/j.compbiomed.2023.107064].2023 An Efficient Implementation of Flux Variability Analysis for Metabolic Networks Galuzzi, B., Damiani, C. (2023). An Efficient Implementation of Flux Variability Analysis for Metabolic Networks. In Artificial Life and Evolutionary Computation – 16th Italian Workshop, WIVACE 2022, Gaeta, Italy, September 14–16, 2022, Revised Selected Papers (pp.58-69). Springer [10.1007/978-3-031-31183-3_5].2023 Tumor heterogeneity: preclinical models, emerging technologies, and future applications Proietto, M., Crippa, M., Damiani, C., Pasquale, V., Sacco, E., Vanoni, M., et al. (2023). Tumor heterogeneity: preclinical models, emerging technologies, and future applications. FRONTIERS IN ONCOLOGY, 13, 1-24 [10.3389/fonc.2023.1164535].2023 Exploring the Solution Space of Cancer Evolution Inference Frameworks for Single-Cell Sequencing Data Maspero, D., Angaroni, F., Patruno, L., Ramazzotti, D., Posada, D., Graudenzi, A. (2023). Exploring the Solution Space of Cancer Evolution Inference Frameworks for Single-Cell Sequencing Data. In Artificial Life and Evolutionary Computation 16th Italian Workshop, WIVACE 2022, Gaeta, Italy, September 14–16, 2022, Revised Selected Papers (pp.70-81). Springer [10.1007/978-3-031-31183-3_6].2023 Best Practices in Flux Sampling of Constrained-Based Models Galuzzi, B., Milazzo, L., Damiani, C. (2023). Best Practices in Flux Sampling of Constrained-Based Models. In Machine Learning, Optimization, and Data Science 8th International Conference, LOD 2022, Certosa di Pontignano, Italy, September 18–22, 2022, Revised Selected Papers, Part II (pp.234-248). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-25891-6_18].2023 LACE 2.0: an interactive R tool for the inference and visualization of longitudinal cancer evolution Ascolani, G., Angaroni, F., Maspero, D., Craighero, F., Bhavesh, N., Piazza, R., et al. (2023). LACE 2.0: an interactive R tool for the inference and visualization of longitudinal cancer evolution. BMC BIOINFORMATICS, 24(1) [10.1186/s12859-023-05221-3].2023 Characterization of SARS-CoV-2 Mutational Signatures from 1.5+ Million Raw Sequencing Samples Aroldi, A., Angaroni, F., D’Aliberti, D., Spinelli, S., Crespiatico, I., Crippa, V., et al. (2023). Characterization of SARS-CoV-2 Mutational Signatures from 1.5+ Million Raw Sequencing Samples. VIRUSES, 15(1) [10.3390/v15010007].

2022

2022 Combining denoising of RNA-seq data and flux balance analysis for cluster analysis of single cells Galuzzi, B., Vanoni, M., Damiani, C. (2022). Combining denoising of RNA-seq data and flux balance analysis for cluster analysis of single cells. BMC BIOINFORMATICS, 23(Suppl 6) [10.1186/s12859-022-04967-6].2022 Machine Learning for Efficient Prediction of Protein Redox Potential: The Flavoproteins Case Galuzzi, B., Mirarchi, A., Viganò, E., De Gioia, L., Damiani, C., Arrigoni, F. (2022). Machine Learning for Efficient Prediction of Protein Redox Potential: The Flavoproteins Case. JOURNAL OF CHEMICAL INFORMATION AND MODELING, 62(19), 4748-4759 [10.1021/acs.jcim.2c00858].2022 J-SPACE: a Julia package for the simulation of spatial models of cancer evolution and of sequencing experiments Angaroni, F., Guidi, A., Ascolani, G., D’Onofrio, A., Antoniotti, M., Graudenzi, A. (2022). J-SPACE: a Julia package for the simulation of spatial models of cancer evolution and of sequencing experiments. BMC BIOINFORMATICS, 23(1) [10.1186/s12859-022-04779-8].2022 Early detection and improved genomic surveillance of SARS-CoV-2 variants from deep sequencing data Ramazzotti, D., Maspero, D., Angaroni, F., Spinelli, S., Antoniotti, M., Piazza, R., et al. (2022). Early detection and improved genomic surveillance of SARS-CoV-2 variants from deep sequencing data. ISCIENCE, 25(6 (17 June 2022)) [10.1016/j.isci.2022.104487].2022 SparseSignatures: An R package using LASSO-regularized non-negative matrix factorization to identify mutational signatures from human tumor samples Mella, L., Lal, A., Angaroni, F., Maspero, D., Piazza, R., Sidow, A., et al. (2022). SparseSignatures: An R package using LASSO-regularized non-negative matrix factorization to identify mutational signatures from human tumor samples. STAR PROTOCOLS, 3(3) [10.1016/j.xpro.2022.101513].2022 Variant calling from scRNA-seq data allows the assessment of cellular identity in patient-derived cell lines Ramazzotti, D., Angaroni, F., Maspero, D., Ascolani, G., Castiglioni, I., Piazza, R., et al. (2022). Variant calling from scRNA-seq data allows the assessment of cellular identity in patient-derived cell lines. NATURE COMMUNICATIONS, 13(1 (December 2022)) [10.1038/s41467-022-30230-w].2022 Chemotherapy after PD-1 inhibitors in relapsed/refractory Hodgkin lymphoma: Outcomes and clonal evolution dynamics Calabretta, E., Guidetti, A., Ricci, F., Di Trani, M., Monfrini, C., Magagnoli, M., et al. (2022). Chemotherapy after PD-1 inhibitors in relapsed/refractory Hodgkin lymphoma: Outcomes and clonal evolution dynamics. BRITISH JOURNAL OF HAEMATOLOGY, 198(1), 82-92 [10.1111/bjh.18183].2022 Large-Scale Analysis of SARS-CoV-2 Synonymous Mutations Reveals the Adaptation to the Human Codon Usage During the Virus Evolution Ramazzotti, D., Angaroni, F., Maspero, D., Mauri, M., D’Aliberti, D., Fontana, D., et al. (2022). Large-Scale Analysis of SARS-CoV-2 Synonymous Mutations Reveals the Adaptation to the Human Codon Usage During the Virus Evolution. VIRUS EVOLUTION, 8(1) [10.1093/ve/veac026].2022 INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation Di Filippo, M., Pescini, D., Galuzzi, B., Bonanomi, M., Gaglio, D., Mangano, E., et al. (2022). INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation. PLOS COMPUTATIONAL BIOLOGY, 18(2) [10.1371/journal.pcbi.1009337].2022 LACE: Inference of cancer evolution models from longitudinal single-cell sequencing data Ramazzotti, D., Angaroni, F., Maspero, D., Ascolani, G., Castiglioni, I., Piazza, R., et al. (2022). LACE: Inference of cancer evolution models from longitudinal single-cell sequencing data. JOURNAL OF COMPUTATIONAL SCIENCE, 58(February 2022) [10.1016/j.jocs.2021.101523].2022 PMCE: efficient inference of expressive models of cancer evolution with high prognostic power Angaroni, F., Chen, K., Damiani, C., Caravagna, G., Graudenzi, A., Ramazzotti, D. (2022). PMCE: efficient inference of expressive models of cancer evolution with high prognostic power. BIOINFORMATICS, 38(3), 754-762 [10.1093/bioinformatics/btab717].

2021

2021 Mutational signatures and heterogeneous host response revealed via large-scale characterization of SARS-CoV-2 genomic diversity Graudenzi, A., Maspero, D., Angaroni, F., Piazza, R., Ramazzotti, D. (2021). Mutational signatures and heterogeneous host response revealed via large-scale characterization of SARS-CoV-2 genomic diversity. ISCIENCE, 24(2) [10.1016/j.isci.2021.102116].2021 GPRuler: Metabolic gene-protein-reaction rules automatic reconstruction Di Filippo, M., Damiani, C., Pescini, D. (2021). GPRuler: Metabolic gene-protein-reaction rules automatic reconstruction. PLOS COMPUTATIONAL BIOLOGY, 17(11) [10.1371/journal.pcbi.1009550].2021 VirMutSig: Discovery and assignment of viral mutational signatures from sequencing data Maspero, D., Angaroni, F., Porro, D., Piazza, R., Graudenzi, A., Ramazzotti, D. (2021). VirMutSig: Discovery and assignment of viral mutational signatures from sequencing data. STAR PROTOCOLS, 2(4) [10.1016/j.xpro.2021.100911].2021 OG-SPACE: Optimized Stochastic Simulation of Spatial Models of Cancer Evolution Angaroni, F., Antoniotti, M., Graudenzi, A. (2021). OG-SPACE: Optimized Stochastic Simulation of Spatial Models of Cancer Evolution [Altro].2021 Optimal Control of a Discrete Time Stochastic Model of an Epidemic Spreading in Arbitrary Networks Angaroni, F., Damiani, C., Ramunni, G., Antoniotti, M. (2021). Optimal Control of a Discrete Time Stochastic Model of an Epidemic Spreading in Arbitrary Networks. In Proceedings of the 2021 Annual Modeling and Simulation Conference, ANNSIM 2021 (pp.1-12). IEEE [10.23919/ANNSIM52504.2021.9552097].2021 Combining multi-target regression deep neural networks and kinetic modeling to predict relative fluxes in reaction systems Patruno, L., Craighero, F., Maspero, D., Graudenzi, A., Damiani, C. (2021). Combining multi-target regression deep neural networks and kinetic modeling to predict relative fluxes in reaction systems. INFORMATION AND COMPUTATION, 281 [10.1016/j.ic.2021.104798].2021 Accelerated global sensitivity analysis of genome-wide constraint-based metabolic models Nobile, M., Coelho, V., Pescini, D., Damiani, C. (2021). Accelerated global sensitivity analysis of genome-wide constraint-based metabolic models. BMC BIOINFORMATICS, 22(April 2021) [10.1186/s12859-021-04002-0].2021 On the use of topological features of metabolic networks for the classification of cancer samples Machicao, J., Craighero, F., Maspero, D., Angaroni, F., Damiani, C., Graudenzi, A., et al. (2021). On the use of topological features of metabolic networks for the classification of cancer samples. CURRENT GENOMICS, 22(2), 88-97 [10.2174/1389202922666210301084151].2021 VERSO: a comprehensive framework for the inference of robust phylogenies and the quantification of intra-host genomic diversity of viral samples Ramazzotti., D., Angaroni, F., Maspero, D., Gambacorti-Passerini, C., Antoniotti, M., Graudenzi, A., et al. (2021). VERSO: a comprehensive framework for the inference of robust phylogenies and the quantification of intra-host genomic diversity of viral samples. PATTERNS, 2(3 (12 March 2021)) [10.1016/j.patter.2021.100212].2021 A review of computational strategies for denoising and imputation of single-cell transcriptomic data Patruno, L., Maspero, D., Craighero, F., Angaroni, F., Antoniotti, M., Graudenzi, A. (2021). A review of computational strategies for denoising and imputation of single-cell transcriptomic data. BRIEFINGS IN BIOINFORMATICS, 22(4 (July 2021)) [10.1093/bib/bbaa222].

2020

2020 The detection of dynamical organization in cancer evolution models Sani, L., D’Addese, G., Graudenzi, A., Villani, M. (2020). The detection of dynamical organization in cancer evolution models. In Artificial Life and Evolutionary Computation. 14th Italian Workshop, WIVACE 2019, Rende, Italy, September 18–20, 2019, Revised Selected Papers (pp.49-61). Springer [10.1007/978-3-030-45016-8_6].2020 Read-through transcripts in lung: Germline genetic regulation and correlation with the expression of other genes Maspero, D., Dassano, A., Pintarelli, G., Noci, S., de Cecco, L., Incarbone, M., et al. (2020). Read-through transcripts in lung: Germline genetic regulation and correlation with the expression of other genes. CARCINOGENESIS, 41(7), 918-926 [10.1093/CARCIN/BGAA020].2020 Integration of single-cell RNA-sequencing data into flux balance cellular automata Maspero, D., Di Filippo, M., Angaroni, F., Pescini, D., Mauri, G., Vanoni, M., et al. (2020). Integration of single-cell RNA-sequencing data into flux balance cellular automata. In CIBB 2019 – Computational Intelligence methods for Bioinformatics and Biostatistics, Revised selected papers (pp.207-215). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-030-63061-4_19].2020 Understanding deep learning with activation pattern diagrams Craighero, F., Angaroni, F., Graudenzi, A., Stella, F., Antoniotti, M. (2020). Understanding deep learning with activation pattern diagrams. In CEUR Workshop Proceedings (pp.119-126). CEUR-WS.2020 A closed-loop optimization framework for personalized cancer therapy design Angaroni, F., Pennati, M., Patruno, L., Maspero, D., Antoniotti, M., Graudenzi, A. (2020). A closed-loop optimization framework for personalized cancer therapy design. In 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) (pp.1-9) [10.1109/CIBCB48159.2020.9277647].2020 VERSO:A COMPREHENSIVE FRAMEWORK FOR THE INFERENCE OF ROBUST PHYLOGENIES AND THE QUANTIFICATION OF INTRA-HOST GENOMIC DIVERSITY OF VIRAL SAMPLES Ramazzotti, D., Angaroni, F., Maspero, D., Gambacorti-Passerini, C., Antoniotti, M., Graudenzi, A., et al. (2020). VERSO:A COMPREHENSIVE FRAMEWORK FOR THE INFERENCE OF ROBUST PHYLOGENIES AND THE QUANTIFICATION OF INTRA-HOST GENOMIC DIVERSITY OF VIRAL SAMPLES [Altro] [10.1101/2020.04.22.044404].2020 Systems metabolomics: from metabolomic snapshots to design principles Damiani, C., Gaglio, D., Sacco, E., Alberghina, L., Vanoni, M. (2020). Systems metabolomics: from metabolomic snapshots to design principles. CURRENT OPINION IN BIOTECHNOLOGY, 63, 190-199 [10.1016/j.copbio.2020.02.013].2020 On the automatic calibration of fully analogical spiking neuromorphic chips Papetti, D., Spolaor, S., Besozzi, D., Cazzaniga, P., Antoniotti, M., Nobile, M. (2020). On the automatic calibration of fully analogical spiking neuromorphic chips. In 2020 International Joint Conference on Neural Networks (IJCNN). Institute of Electrical and Electronics Engineers Inc. [10.1109/IJCNN48605.2020.9206654].2020 SBML Level 3: an extensible format for the exchange and reuse of biological models Keating, S., Waltemath, D., König, M., Zhang, F., Dräger, A., Chaouiya, C., et al. (2020). SBML Level 3: an extensible format for the exchange and reuse of biological models. MOLECULAR SYSTEMS BIOLOGY, 16(8) [10.15252/msb.20199110].2020 An Optimal Control Framework for the Automated Design of Personalized Cancer Treatments Angaroni, F., Graudenzi, A., Rossignolo, M., Maspero, D., Calarco, T., Piazza, R., et al. (2020). An Optimal Control Framework for the Automated Design of Personalized Cancer Treatments. FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY, 8 [10.3389/fbioe.2020.00523].2020 Investigating the Compositional Structure Of Deep Neural Networks Craigher, F., Angaroni, F., Graudenzi, A., Stella, F., Antoniotti, M. (2020). Investigating the Compositional Structure Of Deep Neural Networks. In The Sixth International Conference on Machine Learning, Optimization, and Data Science (pp.322-334) [10.1007/978-3-030-64583-0_30].2020 Characterization of intra-host SARS-CoV-2 variants improves phylogenomic reconstruction and may reveal functionally convergent mutations Ramazzotti, D., Angaroni, F., Maspero, D., Gambacorti-Passerini, C., Antoniotti, M., Graudenzi, A., et al. (2020). Characterization of intra-host SARS-CoV-2 variants improves phylogenomic reconstruction and may reveal functionally convergent mutations [Rapporto tecnico] [10.1101/2020.04.22.044404].2020 MaREA4Galaxy: metabolic reaction enrichment analysis and visualization of RNA-seq data using Galaxy Damiani, C., Rovida, L., Maspero, D., Sala, I., Rosato, L., Di Filippo, M., et al. (2020). MaREA4Galaxy: metabolic reaction enrichment analysis and visualization of RNA-seq data using Galaxy. COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, 18, 993-999 [10.1016/j.csbj.2020.04.008].2020 Application of Random Forest algorithm for Prediction of False positive Insertion sites in gene therapy treated patient’s integrome profile Omrani, M., Calabria, A., Antoniotti, M., Aiuti, A. (2020). Application of Random Forest algorithm for Prediction of False positive Insertion sites in gene therapy treated patient’s integrome profile. Intervento presentato a: RECOMB – Computational Cancer Biology 2020, Padua, Italy.2020 Why You Cannot (Yet) Write an “Interval Arithmetic” Library in Common Lisp — or: Hammering Some Sense into :ieee-floating-point Antoniotti, M. (2020). Why You Cannot (Yet) Write an “Interval Arithmetic” Library in Common Lisp — or: Hammering Some Sense into :ieee-floating-point. Intervento presentato a: European Lisp Symposium 2020, Zurich, Switzerland [10.5281/zenodo.3759522].2020 Global Sensitivity Analysis of Constraint-Based Metabolic Models Damiani, C., Pescini, D., Nobile, M. (2020). Global Sensitivity Analysis of Constraint-Based Metabolic Models. In Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2018 (pp.179-186). Cham : Springer [10.1007/978-3-030-34585-3_16].2020 Single-cell digital twins for cancer preclinical investigation Di Filippo, M., Damiani, C., Vanoni, M., Maspero, D., Mauri, G., Alberghina, L., et al. (2020). Single-cell digital twins for cancer preclinical investigation. In Nagrath D. (a cura di), Metabolic Flux Analysis in Eukaryotic Cells (pp. 331-343). New York : Humana Press Inc. [10.1007/978-1-0716-0159-4_15].2020 Longitudinal cancer evolution from single cells Ramazzotti, D., Angaroni, F., Maspero, D., Ascolani, G., Castiglioni, I., Piazza, R., et al. (2020). Longitudinal cancer evolution from single cells [Rapporto tecnico] [10.1101/2020.01.14.906453].2020 Network Bioscience Pellegrini, M., Antoniotti, M., Mishra, M. (a cura di). (2020). Network Bioscience. Frontiers Media SA, Lausanne, CH [10.3389/978-2-88963-289-3].2020 FBCA, A multiscale modeling framework combining cellular automata and flux balance analysis Graudenzi, A., Maspero, D., Damiani, C. (2020). FBCA, A multiscale modeling framework combining cellular automata and flux balance analysis. JOURNAL OF CELLULAR AUTOMATA, 15(1-2), 75-95.

2019

2019 Cigarette smoke alters the transcriptome of non-involved lung tissue in lung adenocarcinoma patients Pintarelli, G., Noci, S., Maspero, D., Pettinicchio, A., Dugo, M., De Cecco, L., et al. (2019). Cigarette smoke alters the transcriptome of non-involved lung tissue in lung adenocarcinoma patients. SCIENTIFIC REPORTS, 9(1) [10.1038/s41598-019-49648-2].2019 The Influence of Nutrients Diffusion on a Metabolism-driven Model of a Multi-cellular System Maspero, D., Damiani, C., Antoniotti, M., Graudenzi, A., Di Filippo, M., Vanoni, M., et al. (2019). The Influence of Nutrients Diffusion on a Metabolism-driven Model of a Multi-cellular System. FUNDAMENTA INFORMATICAE, 171(1-4), 279-295 [10.3233/FI-2020-1883].2019 Editorial: Network Bioscience Antoniotti, M., Mishra, B., Pellegrini, M. (2019). Editorial: Network Bioscience. FRONTIERS IN GENETICS, 10 [10.3389/fgene.2019.01160].2019 ISwap: a bioinformatics tool for index switching detection in vector integration site studies De Marino, A., Calabria, A., Benedicenti, F., Antoniotti, M., Montini, E. (2019). ISwap: a bioinformatics tool for index switching detection in vector integration site studies. Intervento presentato a: Computational Intelligence methods for Bioinformatics and Biostatistics 2019 (CIBB 2019), Bergamo.2019 cyTRON and cyTRON/JS: two Cytoscape-based applications for the inference of cancer evolution models Patruno, L., Galimberti, E., Ramazzotti, D., Caravagna, G., De Sano, L., Antoniotti, M., et al. (2019). cyTRON and cyTRON/JS: two Cytoscape-based applications for the inference of cancer evolution models. Intervento presentato a: Computational Intelligence methods for Bioinformatics and Biostatistics 2019, Bergamo, Italy.2019 Personalized therapy design for liquid tumors via optimal control theory Angaroni, F., Graudenzi, A., Rossignolo, M., Maspero, D., Calarco, T., Piazza, R., et al. (2019). Personalized therapy design for liquid tumors via optimal control theory [Altro] [10.1101/662858].2019 Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data Ramazzotti, D., Graudenzi, A., De Sano, L., Antoniotti, M., Caravagna, G. (2019). Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data. BMC BIOINFORMATICS, 20(1) [10.1186/s12859-019-2795-4].2019 Integration of single-cell RNA-seq data into population models to characterize cancer metabolism Damiani, C., Maspero, D., Di Filippo, M., Colombo, R., Pescini, D., Graudenzi, A., et al. (2019). Integration of single-cell RNA-seq data into population models to characterize cancer metabolism. PLOS COMPUTATIONAL BIOLOGY, 15(2) [10.1371/journal.pcbi.1006733].2019 Efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena Ramazzotti, D., Nobile, M., Antoniotti, M., Graudenzi, A. (2019). Efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena. JOURNAL OF COMPUTATIONAL SCIENCE, 30, 1-10 [10.1016/j.jocs.2018.10.009].2019 Synchronization effects in a metabolism-driven model of multi-cellular system Maspero, D., Graudenzi, A., AMARPAL SINGH, A., Pescini, D., Mauri, G., Antoniotti, M., et al. (2019). Synchronization effects in a metabolism-driven model of multi-cellular system. In 13th Italian Workshop, WIVACE 2018, Parma, Italy, September 10–12, 2018, Revised Selected Papers (pp.115-126) [10.1007/978-3-030-21733-4_9].

2018

2018 Modeling Spatio-Temporal Dynamics of Metabolic Networks with Cellular Automata and Constraint-Based Methods Graudenzi, A., Maspero, D., Damiani, C. (2018). Modeling Spatio-Temporal Dynamics of Metabolic Networks with Cellular Automata and Constraint-Based Methods. In Cellular Automata 13th International Conference on Cellular Automata for Research and Industry, ACRI 2018, Como, Italy, September 17–21, 2018, Proceedings Part of the book series: Theoretical Computer Science and General Issues (LNTCS, volume 11115) Part of the Lecture Notes in Computer Science book series (LNTCS,volume 11115) Conference series link(s): ACRI: International Conference on Cellular Automata for Research and Industry (pp.16-29). Springer Verlag [10.1007/978-3-319-99813-8_2].2018 GDNA qPCR is statistically more reliable than mRNA analysis in detecting leukemic cells to monitor CML Rainero, A., Angaroni, F., Conti, A., Pirrone, C., Micheloni, G., Tarara, L., et al. (2018). GDNA qPCR is statistically more reliable than mRNA analysis in detecting leukemic cells to monitor CML. CELL DEATH & DISEASE, 9(3) [10.1038/s41419-018-0387-2].2018 Integration of transcriptomic data and metabolic networks in cancer samples reveals highly significant prognostic power Graudenzi, A., Maspero, D., Di Filippo, M., Gnugnoli, M., Isella, C., Mauri, G., et al. (2018). Integration of transcriptomic data and metabolic networks in cancer samples reveals highly significant prognostic power. JOURNAL OF BIOMEDICAL INFORMATICS, 87, 37-49 [10.1016/j.jbi.2018.09.010].2018 Modeling cumulative biological phenomena with Suppes-Bayes causal networks Ramazzotti, D., Graudenzi, A., Caravagna, G., Antoniotti, M. (2018). Modeling cumulative biological phenomena with Suppes-Bayes causal networks. EVOLUTIONARY BIOINFORMATICS ONLINE, 14, 1-10 [10.1177/1176934318785167].2018 Structural Learning of Probabilistic Graphical Models of Cumulative Phenomena Ramazzotti, D., Nobile, M., Antoniotti, M., Graudenzi, A. (2018). Structural Learning of Probabilistic Graphical Models of Cumulative Phenomena. In Computational Science 2018 – ICCS 2018 (pp.678-693) [10.1007/978-3-319-93698-7_52].2018 Omics and Clinical Data Integration De Sanctis, G., Colombo, R., Damiani, C., Sacco, E., Vanoni, M. (2018). Omics and Clinical Data Integration. In A. Vlahou, H. Mischak, J. Zoidakis, F. Magni (a cura di), Integration of Omics Approaches and Systems Biology for Clinical Applications (pp. 248-273). Hoboken : John Wiley & Sons [10.1002/9781119183952.ch15].2018 Emerging ensembles of kinetic parameters to characterize observed metabolic phenotypes Colombo, R., Damiani, C., Gilbert, D., Heiner, M., Mauri, G., Pescini, D. (2018). Emerging ensembles of kinetic parameters to characterize observed metabolic phenotypes. BMC BIOINFORMATICS, 19(Suppl 7), 45-59 [10.1186/s12859-018-2181-7].

2017

2017 Amplification of the parametric dynamical Casimir effect via optimal control Hoeb, F., Angaroni, F., Zoller, J., Calarco, T., Strini, G., Montangero, S., et al. (2017). Amplification of the parametric dynamical Casimir effect via optimal control. PHYSICAL REVIEW A, 96(3) [10.1103/PhysRevA.96.033851].2017 Dynamical regimes in non-ergodic random Boolean networks Villani, M., Campioli, D., Damiani, C., Roli, A., Filisetti, A., Serra, A. (2017). Dynamical regimes in non-ergodic random Boolean networks. NATURAL COMPUTING, 16(2), 353-363 [10.1007/s11047-016-9552-7].2017 COSYS: A computational infrastructure for systems biology Cumbo, F., Nobile, M., Damiani, C., Colombo, R., Mauri, G., Cazzaniga, P. (2017). COSYS: A computational infrastructure for systems biology. In CIBB 2016 – 13th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, Revised Selected Papers (pp.82-92). Springer Verlag [10.1007/978-3-319-67834-4_7].2017 Constraining mechanism based simulations to identify ensembles of parametrizations to characterize metabolic features Colombo, R., Damiani, C., Mauri, G., Pescini, D. (2017). Constraining mechanism based simulations to identify ensembles of parametrizations to characterize metabolic features. In Computational Intelligence Methods for Bioinformatics and Biostatistics. 13th International Meeting, CIBB 2016, Stirling, UK, September 1-3, 2016, Revised Selected Papers (pp.107-117). Springer Verlag [10.1007/978-3-319-67834-4_9].2017 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 Damiani, C., Colombo, R., Gaglio, D., Mastroianni, F., Pescini, D., Westerhoff, H., et al. (2017). 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. PLOS COMPUTATIONAL BIOLOGY, 13(9), 1-29 [10.1371/journal.pcbi.1005758].2017 A computational framework to infer the order of accumulating mutations in individual tumors Ramazzotti, D., Graudenzi, A., De Sano, L., Antoniotti, M., Caravagna, G. (2017). A computational framework to infer the order of accumulating mutations in individual tumors. Intervento presentato a: Base Computational Biology Conference [BC]^2, Basilea.2017 popFBA: tackling intratumour heterogeneity with Flux Balance Analysis Damiani, C., Di Filippo, M., Pescini, D., Maspero, D., Colombo, R., Mauri, G. (2017). popFBA: tackling intratumour heterogeneity with Flux Balance Analysis. BIOINFORMATICS, 33(14), i311-i318 [10.1093/bioinformatics/btx251].2017 Constraint-based modeling and simulation of cell populations Di Filippo, M., Damiani, C., Colombo, R., Pescini, D., Mauri, G. (2017). Constraint-based modeling and simulation of cell populations. In Advances in Artificial Life, Evolutionary Computation, and Systems Chemistry. 11th Italian Workshop, WIVACE 2016, Fisciano, Italy, October 4-6, 2016, Revised Selected Papers (pp.126-137). Springer Verlag [10.1007/978-3-319-57711-1_11].2017 Pathway-based classification of breast cancer subtypes Graudenzi, A., Cava, C., Bertoli, G., Fromm, B., Flatmark, K., Mauri, G., et al. (2017). Pathway-based classification of breast cancer subtypes. FRONTIERS IN BIOSCIENCE, 22(10), 1697-1712 [10.2741/4566].2017 Linking alterations in metabolic fluxes with shifts in metabolite levels by means of kinetic modeling Damiani, C., Colombo, R., Di Filippo, M., Pescini, D., Mauri, G. (2017). Linking alterations in metabolic fluxes with shifts in metabolite levels by means of kinetic modeling. In Advances in Artificial Life, Evolutionary Computation, and Systems Chemistry. 11th Italian Workshop, WIVACE 2016, Fisciano, Italy, October 4-6, 2016, Revised Selected Papers (pp.138-148). Springer Verlag [10.1007/978-3-319-57711-1_12].2017 A Computational Framework To Infer The Order Of Accumulating Mutations In Individual Tumors Ramazzotti, D., Graudenzi, A., De Sano, L., Antoniotti, M., Caravagna, G. (2017). A Computational Framework To Infer The Order Of Accumulating Mutations In Individual Tumors [Altro] [10.1101/132183].2017 SpidermiR: An R/bioconductor package for integrative analysis with miRNA data Cava, C., Colaprico, A., Bertoli, G., Graudenzi, A., Silva, T., Olsen, C., et al. (2017). SpidermiR: An R/bioconductor package for integrative analysis with miRNA data. INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, 18(2), 1-13 [10.3390/ijms18020274].

2016

2016 Parallel implementation of efficient search schemes for the inference of cancer progression models Ramazzotti, D., Nobile, M., Cazzaniga, P., Mauri, G., Antoniotti, M. (2016). Parallel implementation of efficient search schemes for the inference of cancer progression models. In 2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) (pp.1-6). Institute of Electrical and Electronics Engineers Inc. [10.1109/CIBCB.2016.7758109].2016 Design of the TRONCO bioconductor package for TRanslational ONCOlogy Antoniotti, M., Caravagna, G., De Sano, L., Graudenzi, A., Mauri, G., Mishra, B., et al. (2016). Design of the TRONCO bioconductor package for TRanslational ONCOlogy. THE R JOURNAL, 8(2), 39-59 [10.32614/RJ-2016-032].2016 Divergent in vitro/in vivo responses to drug treatments of highly aggressive NIH-Ras cancer cells: A PET imaging and metabolomics-mass-spectrometry study Gaglio, D., Valtorta, S., Ripamonti, M., Bonanomi, M., Damiani, C., Todde, S., et al. (2016). Divergent in vitro/in vivo responses to drug treatments of highly aggressive NIH-Ras cancer cells: A PET imaging and metabolomics-mass-spectrometry study. ONCOTARGET, 7(32), 52017-52031 [10.18632/oncotarget.10470].2016 Algorithmic methods to infer the evolutionary trajectories in cancer progression Caravagna, G., Graudenzi, A., Ramazzotti, D., Sanz Pamplona, R., De Sano, L., Mauri, G., et al. (2016). Algorithmic methods to infer the evolutionary trajectories in cancer progression. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 113(28), E4025-E4034 [10.1073/pnas.1520213113].2016 Zooming-in on cancer metabolic rewiring with tissue specific constraint-based models DI FILIPPO, M., Colombo, R., Damiani, C., Pescini, D., Gaglio, D., Vanoni, M., et al. (2016). Zooming-in on cancer metabolic rewiring with tissue specific constraint-based models. COMPUTATIONAL BIOLOGY AND CHEMISTRY, 62, 60-69 [10.1016/j.compbiolchem.2016.03.002].2016 CABeRNET: A Cytoscape app for augmented Boolean models of gene regulatory NETworks Paroni, A., Graudenzi, A., Caravagna, G., Damiani, C., Mauri, G., Antoniotti, M. (2016). CABeRNET: A Cytoscape app for augmented Boolean models of gene regulatory NETworks. BMC BIOINFORMATICS, 17(1) [10.1186/s12859-016-0914-z].2016 TRONCO: An R package for the inference of cancer progression models from heterogeneous genomic data De Sano, L., Caravagna, G., Ramazzotti, D., Graudenzi, A., Mauri, G., Mishra, B., et al. (2016). TRONCO: An R package for the inference of cancer progression models from heterogeneous genomic data. BIOINFORMATICS, 32(12), 1911-1913 [10.1093/bioinformatics/btw035].2016 Different regulation of miR-29a-3p in glomeruli and tubules in an experimental model of angiotensin II-dependent hypertension: Potential role in renal fibrosis Castoldi, G., Di Gioia, C., Giollo, F., Carletti, R., BOMBARDI ROSA, C., Antoniotti, M., et al. (2016). Different regulation of miR-29a-3p in glomeruli and tubules in an experimental model of angiotensin II-dependent hypertension: Potential role in renal fibrosis. CLINICAL AND EXPERIMENTAL PHARMACOLOGY & PHYSIOLOGY., 43(3), 335-342 [10.1111/1440-1681.12532].2016 Ordering cancer mutational profiles of cross-sectional copy number alterations Graudenzi, A., Caravagna, G., Bocicor, I., Cava, C., Antoniotti, M., Mauri, G. (2016). Ordering cancer mutational profiles of cross-sectional copy number alterations. INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS, 15(1), 59-83 [10.1504/IJDMB.2016.076017].

2015

2015 Cognac: A chaste plugin for the multiscale simulation of gene regulatory networks driving the spatial dynamics of tissues and cancer Rubinacci, S., Graudenzi, A., Caravagna, G., Mauri, G., Osborne, J., Pitt Francis, J., et al. (2015). Cognac: A chaste plugin for the multiscale simulation of gene regulatory networks driving the spatial dynamics of tissues and cancer. CANCER INFORMATICS, 14(Suppl. 4), 53-65 [10.4137/CIN.S19965].2015 Automatising the analysis of stochastic biochemical time-series Caravagna, G., De Sano, L., Antoniotti, M. (2015). Automatising the analysis of stochastic biochemical time-series. BMC BIOINFORMATICS, 16(9) [10.1186/1471-2105-16-S9-S8].2015 CAPRI: Efficient Inference of Cancer Progression Models from Cross-sectional Data Ramazzotti, D., Caravagna, G., Olde Loohuis, L., Graudenzi, A., Korsunsky, I., Mauri, G., et al. (2015). CAPRI: Efficient Inference of Cancer Progression Models from Cross-sectional Data. BIOINFORMATICS, 31(18), 3016-3026 [10.1093/bioinformatics/btv296].

Publications

Contacts