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2024
2024
Integrated visualization of metabolomics and transcriptomics with Galaxy
Ferrari, M., Lapi, F., Penati, L., Vanoni, M., Galuzzi, B., Damiani, C. (2024). Integrated visualization of metabolomics and transcriptomics with Galaxy. Intervento presentato a: 19th conference on Computational Intelligence methods for Bioinformatics and Biostatistics (CIBB), Benevento, Italia.
2024
Perspectives on computational modeling of biological systems and the significance of the SysMod community
Puniya, B., Verma, M., Damiani, C., Bakr, S., Drager, A. (2024). Perspectives on computational modeling of biological systems and the significance of the SysMod community. BIOINFORMATICS ADVANCES, 4(1) [10.1093/bioadv/vbae090].
2024
Clonal Lineage Tracing with Somatic Delivery of Recordable Barcodes Reveals Migration Histories of Metastatic Prostate Cancer
Serio, R., Scheben, A., Lu, B., Gargiulo, D., Patruno, L., Buckholtz, C., et al. (2024). Clonal Lineage Tracing with Somatic Delivery of Recordable Barcodes Reveals Migration Histories of Metastatic Prostate Cancer. CANCER DISCOVERY [10.1158/2159-8290.cd-23-1332].
2024
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. (2024). Scalable Integration of Multiomic Single Cell Data Using Generative Adversarial Networks. BIOINFORMATICS, 40(5) [10.1093/bioinformatics/btae300].
2024
scFBApy: A Python Framework for Super-Network Flux Balance Analysis
Galuzzi, B., Damiani, C. (2024). scFBApy: A Python Framework for Super-Network Flux Balance Analysis. In Artificial Life and Evolutionary Computation 17th Italian Workshop, WIVACE 2023, Venice, Italy, September 6–8, 2023, Revised Selected Papers (pp.88-97). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-57430-6_8].
2024
Adjusting for false discoveries in constraint-based differential metabolic flux analysis
Galuzzi, B., Milazzo, L., Damiani, C. (2024). Adjusting for false discoveries in constraint-based differential metabolic flux analysis. JOURNAL OF BIOMEDICAL INFORMATICS, 150(February 2024) [10.1016/j.jbi.2024.104597].
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
Scalable integration of Multiomic single cell data using Generative Adversarial Networks
Giansanti, V., Antoniotti, M., Cittaro, D. (2022). Scalable integration of Multiomic single cell data using Generative Adversarial Networks. Intervento presentato a: 2022 European Human Genetics Conference (ESHG2022) – June 11–14, 2022, Vienna, Austria – Hybrid Conference.
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 within 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 within 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 D. Nagrath (a cura di), Metabolic Flux Analysis in Eukaryotic Cells Methods and Protocols (pp. 331-343). 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].
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