Santo Motta

University of Catania — Italy

Biochemistry, Genetics and Molecular Biology · Life Sciences

31h-index2.5kcitations132works0.02yr avg
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Research Topics

vaccines and immunoinformatics approaches(40), Immunotherapy and Immune Responses(31), Monoclonal and Polyclonal Antibodies Research(22), T-cell and B-cell Immunology(18), Gene Regulatory Network Analysis(16)

Publications132 total

Supplementary Tables 1-4 from <i>In silico</i> Modeling and <i>In vivo</i> Efficacy of Cancer-Preventive Vaccinations
2023Open Access
Supplementary Tables 1-4 from <i>In silico</i> Modeling and <i>In vivo</i> Efficacy of Cancer-Preventive Vaccinations
2023Open Access
Data from <i>In silico</i> Modeling and <i>In vivo</i> Efficacy of Cancer-Preventive Vaccinations
2023Open Access
Supplementary Figure Legend from <i>In silico</i> Modeling and <i>In vivo</i> Efficacy of Cancer-Preventive Vaccinations
2023Open Access
Data from <i>In silico</i> Modeling and <i>In vivo</i> Efficacy of Cancer-Preventive Vaccinations
2023Open Access
Supplementary Methods from <i>In silico</i> Modeling and <i>In vivo</i> Efficacy of Cancer-Preventive Vaccinations
2023Open Access
Supplementary Methods from <i>In silico</i> Modeling and <i>In vivo</i> Efficacy of Cancer-Preventive Vaccinations
2023Open Access
Supplementary Figure 1 from <i>In silico</i> Modeling and <i>In vivo</i> Efficacy of Cancer-Preventive Vaccinations
2023Open Access
Supplementary Figure 1 from <i>In silico</i> Modeling and <i>In vivo</i> Efficacy of Cancer-Preventive Vaccinations
2023Open Access
Supplementary Figure Legend from <i>In silico</i> Modeling and <i>In vivo</i> Efficacy of Cancer-Preventive Vaccinations
2023Open Access
CHARACTERIZATION OF OSL AND TL DOSIMETERS WITH DATA COLLECTED AT THE MT25 CYCLIC ELECTRON ACCELERATOR
Physica Medica·2022
Moving forward through the in silico modeling of tuberculosis: a further step with UISS-TB
BMC Bioinformatics·2020· 21 citedOpen Access
The Potential of Computational Modeling to Predict Disease Course and Treatment Response in Patients with Relapsing Multiple Sclerosis
Cells·2020· 43 citedOpen Access
The Potential of Computational Modeling to Predict Disease Course and Treatment Response in Patients with Relapsing Multiple Sclerosis
Preprints.org·2020· 5 citedOpen Access
In silico trial to test COVID-19 candidate vaccines: a case study with UISS platform
Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna)·2020· 53 citedOpen Access
An agent based modeling approach for the analysis of tuberculosis – immune system dynamics
2018· 9 cited
Agent based modeling of relapsing multiple sclerosis: a possible approach to predict treatment outcome
2018· 12 cited
Optimal control in a mathematical model for tumor escape
2018· 1 cited
Tumor escape: A mathematical model
2017· 2 cited
A methodological approach for using high-level Petri Nets to model the immune system response
BMC Bioinformatics·2016· 25 citedOpen Access
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Frequent Co-authors

Francesco Pappalardo(35), Marzio Pennisi(21), Pier‐Luigi Lollini(11), Giulia Russo(10), Filippo Castiglione(10), Vladimir Brusić(5), Arianna Palladini(4), A. M. Anile(4), Davide Alemani(3), Epifanio Fichera(3), Mark Halling‐Brown(3), Ping Zhang(3), Marie‐Paule Lefranc(3), K. E. Basford(3), Adrian J. Shepherd(3), Joel C. Miller(3), G. Godoli(3), Ferdınando Chıacchıo(2), Giordano Nicoletti(2), Annalisa Murgo(2)