Author

Simone Spolaor

University of Milano-BicoccaORCID 0000-0002-3383-367XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Approach:statistical model· 2 Research Focus:cell type annotation· 1 Assay:imaging· 1 Approach:simulation· 1

Frequent Co-Authors

Andrea TangherloniiD· 3 shared Daniela BesozziiD· 3 shared Leonardo RundoiD· 3 shared Marco Salvatore NobileiD· 3 shared Paolo CazzanigaiD· 3 shared Giancarlo MauriiD· 2 shared Alexander L. R. LubbockiD· 1 shared Carlos F. LopeziD· 1 shared

Activity by Year

2020-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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3 shared papersSystems Biology

Andrea TangherloniORCID

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3 papers

Recent·Most cited

SMGen: A generator of synthetic models of biochemical reaction networks

Simone G. Riva, Paolo Cazzaniga, Marco Salvatore Nobile, Simone Spolaor, Leonardo Rundo, Daniela Besozzi, Andrea Tangherloni

SMGen generates synthetic biochemical reaction network models for benchmarking, showing it can create models with 512 species and reactions in under 7 seconds.

Symmetry · Jan 10, 2022Peer-reviewedSystems Biology

FiCoS: a fine- and coarse-grained GPU-powered deterministic simulator for biochemical networks

Andrea Tangherloni, Marco Salvatore Nobile, Paolo Cazzaniga, Giulia Capitoli, Simone Spolaor, Leonardo Rundo, Giancarlo Mauri, Daniela Besozzi

FiCoS is a GPU-powered deterministic simulator for biochemical networks that provides fine- and coarse-grained parallelization, achieving up to 855x speedup over CPU-based simulators.

PLoS Computational Biology · Sep 9, 2021Peer-reviewedSystems Biology

ACDC: Automated Cell Detection and Counting for Time-lapse Fluorescence Microscopy

Leonardo Rundo, Andrea Tangherloni, Darren R. Tyson, Riccardo Betta, Carmelo Militello, Simone Spolaor, Marco Salvatore Nobile, Daniela Besozzi, Alexander L. R. Lubbock, Vito Quaranta, Giancarlo Mauri, Carlos F. Lopez, Paolo Cazzaniga

ACDC automates cell detection and counting in time-lapse microscopy images, achieving high accuracy without large annotated datasets.

Applied Sciences · Sep 6, 2020Peer-reviewedBioinformatics