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Paolo Cazzaniga
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2020 – today
- 2024
- [c50]Andrea Tangherloni, Vasco Coelho, Francesca M. Buffa, Paolo Cazzaniga:
A Modified EACOP Implementation for Real-Parameter Single Objective Optimization Problems. CEC 2024: 1-8 - [c49]Andrea Tangherloni, Paolo Cazzaniga, Nicolò Stranieri, Francesca M. Buffa, Marco S. Nobile:
A Fast Feature Selection for Interpretable Modeling Based on Fuzzy Inference Systems. CIBCB 2024: 1-8 - 2023
- [j36]Daniele M. Papetti
, Andrea Tangherloni
Simplifying Fitness Landscapes Using Dilation Functions Evolved With Genetic Programming. IEEE Comput. Intell. Mag. 18(1): 22-31 (2023) - [j35]Marco S. Nobile, Giulia Capitoli
Unsupervised neural networks as a support tool for pathology diagnosis in MALDI-MSI experiments: A case study on thyroid biopsies. Expert Syst. Appl. 215: 119296 (2023) - [j34]Daniele M. Papetti, Simone Spolaor, Iman Nazari, Andrea Tirelli, Tommaso Leonardi, Chiara Caprioli, Daniela Besozzi, Thalia Vlachou, Pier Giuseppe Pelicci, Paolo Cazzaniga, Marco S. Nobile:
Barcode demultiplexing of nanopore sequencing raw signals by unsupervised machine learning. Frontiers Bioinform. 3 (2023) - [j33]Andrea Tangherloni
MAGNETO: Cell type marker panel generator from single-cell transcriptomic data. J. Biomed. Informatics 147: 104510 (2023) - [c48]Vasco Coelho
The Domination Game: Dilating Bubbles to Fill Up Pareto Fronts. CEC 2023: 1-8 - [c47]Simone G. Riva, Brynelle Myers, Paolo Cazzaniga, Francesca M. Buffa, Andrea Tangherloni
Consensus Clustering Strategy for Cell Type Assignments of scRNA-seq Data. CIBCB 2023: 1-8 - 2022
- [j32]Simone G. Riva
SMGen: A Generator of Synthetic Models of Biochemical Reaction Networks. Symmetry 14(1): 119 (2022) - [c46]Daniele M. Papetti
Local Bubble Dilation Functions: Hypersphere-bounded Landscape Deformations Simplify Global Optimization. CIBCB 2022: 1-8 - [c45]Simone G. Riva, Brynelle Myers, Paolo Cazzaniga, Andrea Tangherloni
A Deep Learning Pipeline for the Automatic cell type Assignment of scRNA-seq Data. CIBCB 2022: 1-8 - [c44]Andrea Tangherloni
Multi-objective Optimization for Marker Panel Identification in Single-cell Data. CIBCB 2022: 1-8 - [e2]Davide Chicco
Computational Intelligence Methods for Bioinformatics and Biostatistics - 17th International Meeting, CIBB 2021, Virtual Event, November 15-17, 2021, Revised Selected Papers. Lecture Notes in Computer Science 13483, Springer 2022, ISBN 978-3-031-20836-2 [contents] - 2021
- [j31]Paolo Cazzaniga, Maria Raposo
Selected papers from the 15th and 16th international conference on Computational Intelligence Methods for Bioinformatics and Biostatistics. BMC Bioinform. 22-S(2): 90 (2021) - [j30]Marco S. Nobile, Paolo Cazzaniga, Daniele Ramazzotti
Investigating the performance of multi-objective optimization when learning Bayesian Networks. Neurocomputing 461: 281-291 (2021) - [j29]Andrea Tangherloni
FiCoS: A fine-grained and coarse-grained GPU-powered deterministic simulator for biochemical networks. PLoS Comput. Biol. 17(9) (2021) - [j28]Leonardo Rundo
A CUDA-powered method for the feature extraction and unsupervised analysis of medical images. J. Supercomput. 77(8): 8514-8531 (2021) - [c43]Simone G. Riva, Paolo Cazzaniga, Andrea Tangherloni
Integration of Multiple scRNA-Seq Datasets on the Autoencoder Latent Space. BIBM 2021: 2155-2162 - [c42]Daniele M. Papetti
If You Can't Beat It, Squash It: Simplify Global Optimization by Evolving Dilation Functions. CEC 2021: 1414-1422 - [c41]Andrea Tangherloni
The Impact of Representation on the Optimization of Marker Panels for Single-cell RNA Data. CEC 2021: 1423-1430 - [c40]Simone Spolaor
A comparison of multi-objective optimization algorithms to identify drug target combinations. CIBCB 2021: 1-8 - 2020
- [j27]Marco S. Nobile
Fuzzy modeling and global optimization to predict novel therapeutic targets in cancer cells. Bioinform. 36(7): 2181-2188 (2020) - [j26]Luca Manzoni
Surfing on Fitness Landscapes: A Boost on Optimization by Fourier Surrogate Modeling. Entropy 22(3): 285 (2020) - [j25]Daniela Besozzi, Luca Manzoni
Computational Intelligence for Life Sciences. Fundam. Informaticae 171(1-4): 57-80 (2020) - [j24]Simone Spolaor
Simpful: A User-Friendly Python Library for Fuzzy Logic. Int. J. Comput. Intell. Syst. 13(1): 1687-1698 (2020) - [j23]Simone Spolaor
Coupling Mechanistic Approaches and Fuzzy Logic to Model and Simulate Complex Systems. IEEE Trans. Fuzzy Syst. 28(8): 1748-1759 (2020) - [j22]Marco S. Nobile
cuProCell: GPU-Accelerated Analysis of Cell Proliferation With Flow Cytometry Data. IEEE J. Biomed. Health Informatics 24(11): 3173-3181 (2020) - [c39]Marco S. Nobile, Simone Spolaor
Which random is the best random? A study on sampling methods in Fourier surrogate modeling. CEC 2020: 1-8 - [c38]Marco S. Nobile, Paolo Cazzaniga, Simone Spolaor
Fourier Surrogate Models of Dilated Fitness Landscapes in Systems Biology : or how we learned to torture optimization problems until they confess. CIBCB 2020: 1-8 - [c37]Daniele M. Papetti
On the automatic calibration of fully analogical spiking neuromorphic chips. IJCNN 2020: 1-8 - [e1]Paolo Cazzaniga
Computational Intelligence Methods for Bioinformatics and Biostatistics - 16th International Meeting, CIBB 2019, Bergamo, Italy, September 4-6, 2019, Revised Selected Papers. Lecture Notes in Computer Science 12313, Springer 2020, ISBN 978-3-030-63060-7 [contents]
2010 – 2019
- 2019
- [j21]Andrea Tangherloni
Biochemical parameter estimation vs. benchmark functions: A comparative study of optimization performance and representation design. Appl. Soft Comput. 81 (2019) - [j20]Marco S. Nobile, Thalia Vlachou, Simone Spolaor
Modeling cell proliferation in human acute myeloid leukemia xenografts. Bioinform. 35(18): 3378-3386 (2019) - [j19]Andrea Tangherloni
GenHap: a novel computational method based on genetic algorithms for haplotype assembly. BMC Bioinform. 20-S(4): 172:1-172:14 (2019) - [j18]Leonardo Rundo
A novel framework for MR image segmentation and quantification by using MedGA. Comput. Methods Programs Biomed. 176: 159-172 (2019) - [j17]Leonardo Rundo
MedGA: A novel evolutionary method for image enhancement in medical imaging systems. Expert Syst. Appl. 119: 387-399 (2019) - [j16]Leonardo Rundo
USE-Net: Incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets. Neurocomputing 365: 31-43 (2019) - [j15]Marco S. Nobile, Paolo Cazzaniga, Daniela Besozzi, Giancarlo Mauri
ginSODA: massive parallel integration of stiff ODE systems on GPUs. J. Supercomput. 75(12): 7844-7856 (2019) - [c36]Marco S. Nobile, Paolo Cazzaniga, Daniel A. Ashlock:
Dilation Functions in Global Optimization. CEC 2019: 2300-2307 - [c35]Marco S. Nobile, Thalia Vlachou, Simone Spolaor
ProCell: Investigating cell proliferation with Swarm Intelligence. CIBCB 2019: 1-8 - [c34]Leonardo Rundo
HaraliCU: GPU-Powered Haralick Feature Extraction on Medical Images Exploiting the Full Dynamics of Gray-Scale Levels. PaCT 2019: 304-318 - [i4]Leonardo Rundo, Changhee Han, Yudai Nagano, Jin Zhang, Ryuichiro Hataya, Carmelo Militello, Andrea Tangherloni, Marco S. Nobile, Claudio Ferretti, Daniela Besozzi, Maria Carla Gilardi, Salvatore Vitabile, Giancarlo Mauri, Hideki Nakayama, Paolo Cazzaniga:
USE-Net: incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets. CoRR abs/1904.08254 (2019) - 2018
- [j14]Marco S. Nobile, Paolo Cazzaniga, Daniela Besozzi, Riccardo Colombo, Giancarlo Mauri
Fuzzy Self-Tuning PSO: A settings-free algorithm for global optimization. Swarm Evol. Comput. 39: 70-85 (2018) - [c33]Marco S. Nobile, Andrea Tangherloni
Computational Intelligence for Parameter Estimation of Biochemical Systems. CEC 2018: 1-8 - [c32]Niccoló Totis
Efficient and Settings-Free Calibration of Detailed Kinetic Metabolic Models with Enzyme Isoforms Characterization. CIBB 2018: 187-202 - [c31]Marco Beccuti, Paolo Cazzaniga, Marzio Pennisi
GPU Accelerated Analysis of Treg-Teff Cross Regulation in Relapsing-Remitting Multiple Sclerosis. Euro-Par Workshops 2018: 626-637 - [c30]Andrea Tangherloni
High Performance Computing for Haplotyping: Models and Platforms. Euro-Par Workshops 2018: 650-661 - [c29]Andrea Tangherloni
GPU-Powered Multi-Swarm Parameter Estimation of Biological Systems: A Master-Slave Approach. PDP 2018: 698-705 - [i3]Paolo Cazzaniga, Marco S. Nobile, Daniele Ramazzotti
Multi-objective optimization to explicitly account for model complexity when learning Bayesian Networks. CoRR abs/1808.01345 (2018) - 2017
- [j13]Marco S. Nobile
Graphics processing units in bioinformatics, computational biology and systems biology. Briefings Bioinform. 18(5): 870-885 (2017) - [j12]Leonard A. Harris
GPU-powered model analysis with PySB/cupSODA. Bioinform. 33(21): 3492-3494 (2017) - [j11]Andrea Tangherloni
LASSIE: simulating large-scale models of biochemical systems on GPUs. BMC Bioinform. 18(1): 246 (2017) - [j10]Marco S. Nobile, Antonio E. Porreca
Efficient Simulation of Reaction Systems on Graphics Processing Units. Fundam. Informaticae 154(1-4): 307-321 (2017) - [j9]Andrea Tangherloni
Gillespie's Stochastic Simulation Algorithm on MIC coprocessors. J. Supercomput. 73(2): 676-686 (2017) - [c28]Simone Spolaor
Estimation of Kinetic Reaction Constants: Exploiting Reboot Strategies to Improve PSO's Performance. CIBB 2017: 92-102 - [c27]Simone Spolaor
Reboot strategies in particle swarm optimization and their impact on parameter estimation of biochemical systems. CIBCB 2017: 1-8 - [i2]Daniele Ramazzotti, Marco S. Nobile, Paolo Cazzaniga, Giancarlo Mauri, Marco Antoniotti:
Parallel Implementation of Efficient Search Schemes for the Inference of Cancer Progression Models. CoRR abs/1703.03038 (2017) - [i1]Andrea Tangherloni, Simone Spolaor, Leonardo Rundo, Marco S. Nobile, Ivan Merelli, Paolo Cazzaniga, Daniela Besozzi, Giancarlo Mauri, Pietro Liò:
GenHap: A novel computational method based on genetic algorithms for haplotype assembly. PeerJ Prepr. 5: e3246 (2017) - 2016
- [c26]Marco S. Nobile, Andrea Tangherloni
GPU-powered and settings-free parameter estimation of biochemical systems. CEC 2016: 32-39 - [c25]Fabio Cumbo
COSYS: A Computational Infrastructure for Systems Biology. CIBB 2016: 82-92 - [c24]Daniele Ramazzotti
Parallel implementation of efficient search schemes for the inference of cancer progression models. CIBCB 2016: 1-6 - [c23]Andrea Tangherloni
GPU-powered Bat Algorithm for the parameter estimation of biochemical kinetic values. CIBCB 2016: 1-6 - 2015
- [c22]Paolo Cazzaniga, Marco S. Nobile, Daniela Besozzi:
The impact of particles initialization in PSO: Parameter estimation as a case in point. CIBCB 2015: 1-8 - [c21]Marco S. Nobile, Gabriella Pasi
Proactive Particles in Swarm Optimization: A self-tuning algorithm based on Fuzzy Logic. FUZZ-IEEE 2015: 1-8 - [c20]Paolo Cazzaniga, F. Ferrara, Marco S. Nobile
Parallelizing Biochemical Stochastic Simulations: A Comparison of GPUs and Intel Xeon Phi Processors. PaCT 2015: 363-374 - 2014
- [j8]Marco S. Nobile
GPU-accelerated simulations of mass-action kinetics models with cupSODA. J. Supercomput. 69(1): 17-24 (2014) - [c19]Marco S. Nobile
A memetic hybrid method for the Molecular Distance Geometry Problem with incomplete information. IEEE Congress on Evolutionary Computation 2014: 1014-1021 - [c18]Matteo Bellini, Daniela Besozzi, Paolo Cazzaniga, Giancarlo Mauri
Simulation and Analysis of the Blood Coagulation Cascade Accelerated on GPU. PDP 2014: 590-593 - 2013
- [j7]Flavio Amara, Riccardo Colombo
In vivo and in silico analysis of PCNA ubiquitylation in the activation of the Post Replication Repair pathway in S. cerevisiae. BMC Syst. Biol. 7: 24 (2013) - [c17]Marco S. Nobile, Daniela Besozzi, Paolo Cazzaniga, Giancarlo Mauri:
The foundation of Evolutionary Petri Nets. BioPPN@Petri Nets 2013: 60-74 - [c16]Marco S. Nobile
Reverse engineering of kinetic reaction networks by means of Cartesian Genetic Programming and Particle Swarm Optimization. IEEE Congress on Evolutionary Computation 2013: 1594-1601 - [c15]Marco S. Nobile, Daniela Besozzi, Paolo Cazzaniga, Giancarlo Mauri, Dario Pescini
cupSODA: A CUDA-Powered Simulator of Mass-Action Kinetics. PaCT 2013: 344-357 - [c14]Daniela Besozzi, Giulio Caravagna
GPU-powered Simulation Methodologies for Biological Systems. WIVACE 2013: 87-91 - 2012
- [j6]Daniela Besozzi, Paolo Cazzaniga, Dario Pescini
The role of feedback control mechanisms on the establishment of oscillatory regimes in the Ras/cAMP/PKA pathway in S. cerevisiae. EURASIP J. Bioinform. Syst. Biol. 2012: 10 (2012) - [c13]Marco S. Nobile
A GPU-Based Multi-swarm PSO Method for Parameter Estimation in Stochastic Biological Systems Exploiting Discrete-Time Target Series. EvoBIO 2012: 74-85 - [c12]Marco S. Nobile
Estimating reaction constants in stochastic biological systems with a multi-swarm PSO running on GPUs. GECCO (Companion) 2012: 1421-1422 - 2011
- [j5]Daniela Besozzi, Paolo Cazzaniga, Stefania Cocolo, Giancarlo Mauri
Modeling Diffusion in a Signal Transduction Pathway: the Use of Virtual Volumes in P Systems. Int. J. Found. Comput. Sci. 22(1): 89-96 (2011) - [c11]Ivan Merelli
Grid Computing for Sensitivity Analysis of Stochastic Biological Models. PaCT 2011: 62-73 - 2010
- [j4]Alberto Leporati
Computing with energy and chemical reactions. Nat. Comput. 9(2): 493-512 (2010) - [c10]Daniela Besozzi, Paolo Cazzaniga, Giancarlo Mauri
BioSimWare: A Software for the Modeling, Simulation and Analysis of Biological Systems. Int. Conf. on Membrane Computing 2010: 119-143 - [c9]Ettore Mosca
Modelling Spatial Heterogeneity and Macromolecular Crowding with Membrane Systems. Int. Conf. on Membrane Computing 2010: 285-304 - [c8]Daniela Besozzi, Paolo Cazzaniga, Dario Pescini
An Analysis on the Influence of Network Topologies on Local and Global Dynamics of Metapopulation Systems. AMCA-POP 2010: 1-17
2000 – 2009
- 2009
- [j3]Daniela Besozzi, Nadia Busi, Paolo Cazzaniga, Claudio Ferretti
(Tissue) P systems with cell polarity. Math. Struct. Comput. Sci. 19(6): 1141-1160 (2009) - [c7]Daniela Besozzi, Paolo Cazzaniga, Dario Pescini
A Multi-volume Approach to Stochastic Modeling with Membrane Systems. Algorithmic Bioprocesses 2009: 519-542 - [c6]Daniela Besozzi, Paolo Cazzaniga, Giancarlo Mauri
A Comparison of Genetic Algorithms and Particle Swarm Optimization for Parameter Estimation in Stochastic Biochemical Systems. EvoBIO 2009: 116-127 - [c5]Paolo Cazzaniga, Giancarlo Mauri
A Novel Variant of P Systems for the Modelling and Simulation of Biochemical Systems. Workshop on Membrane Computing 2009: 210-226 - [c4]Daniela Besozzi, Paolo Cazzaniga, Matteo Dugo, Dario Pescini
A study on the combined interplay between stochastic fluctuations and the number of flagella in bacterial chemotaxis. COMPMOD 2009: 47-62 - 2008
- [j2]Daniela Besozzi, Paolo Cazzaniga, Dario Pescini
Modelling metapopulations with stochastic membrane systems. Biosyst. 91(3): 499-514 (2008) - [c3]Dario Pescini
First Steps Towards a Wet Implementation for t-DPP. Workshop on Membrane Computing 2008: 355-373 - 2007
- [j1]Michael Muskulus
Cycles and communicating classes in membrane systems and molecular dynamics. Theor. Comput. Sci. 372(2-3): 242-266 (2007) - 2006
- [c2]Paolo Cazzaniga, Dario Pescini
Tau Leaping Stochastic Simulation Method in P Systems. Workshop on Membrane Computing 2006: 298-313 - 2005
- [c1]Paolo Cazzaniga, Alberto Leporati
P Systems with Memory. Workshop on Membrane Computing 2005: 165-180
Coauthor Index
[j35] [j34] [c48] [j32] [c46] [j31] [j29] [c42] [c41] [c40] [j27] [j26] [j25] [j24] [j23] [j22] [c39] [c38] [c37] [e1] [j21] [j20] [j19] [j18] [j17] [j16] [j15] [c35] [i4] [j14] [c33] [c32] [c31] [c30] [j13] [j12] [j11] [j10] [j9] [i1] [c26] [c22] [c21] [c20] [j8] [c19] [c18] [j7] [c17] [c16] [c15] [c14] [j6] [c13] [c12] [j5] [j4] [c10] [c8] [j3] [c7] [c6] [c4] [j2] [j1] [c2]
[c49] [j35] [j34] [c48] [j32] [c46] [j30] [j29] [j28] [c42] [c41] [c40] [j27] [j26] [j25] [j24] [j23] [j22] [c39] [c38] [c37] [j21] [j20] [j19] [j18] [j17] [j16] [j15] [c36] [c35] [c34] [i4] [j14] [c33] [c32] [c31] [c30] [c29] [i3] [j13] [j12] [j11] [j10] [j9] [c28] [c27] [i2] [i1] [c26] [c25] [c24] [c23] [c22] [c21] [c20] [j8] [c19] [c18] [c17] [c16] [c15] [c14] [c13] [c12]
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