My mission is to apply computational approaches in a multidisciplinary environment in order to speed-up the drug discovery process, optimise in vitro and in vivo assays, ehance the therapeutic response in cancer and immunotherapies field, and to solve, in general, biomedical issues.
EDUCATION
PhD in Basic and Applied Biomedical Sciences
RESEARCH INTERESTS
Computational modeling in biomedicine and systems biology
110
Scopus Publications
3639
Scholar Citations
32
Scholar h-index
72
Scholar i10-index
Scopus Publications
An integrated in vitro and in silico testing strategy applied to PFAS inhibition of antibody production to define a tolerable daily intake Martina Iulini, Aafke W.F. Janssen, Karsten Beekmann, Giulia Russo, Francesco Pappalardo, Styliani Fragki, Alicia Paini, Emanuela Corsini Toxicology Letters, 2026 Per- and polyfluoroalkyl substances (PFAS) are widely used chemicals known for their persistence, bioaccumulation, and adverse health effects, particularly on the immune system. Epidemiological studies link PFAS exposure to immunosuppression, with increased infection susceptibility and reduced vaccine efficacy. In this paper, we describe the workflow we used to establish an integrated testing strategy (ITS) combining in vitro and in silico methods to model PFAS inhibition of antibody production and to define a tolerable daily intake. This strategy was based on data generated within an EFSA-sponsored project. Using human peripheral blood mononuclear cells, the effects of PFAS on antibody production were assessed. Mathematical models were then applied to determine PFAS free concentrations in vitro , while Physiologically Based Kinetics (PBK) modeling enabled quantitative in vitro to in vivo extrapolation (QIVIVE) to translate in vitro effects into external doses. In addition, the Universal Immune System Simulator was used to predict immune-related outcomes and threshold doses for sensitive populations. Following this strategy, we were able to demonstrate that the oral equivalent effect doses derived through QIVIVE were similar to, or lower than, the tolerable weekly intake established by EFSA for PFAS, indicating that our approach is conservative. We demonstrate the possibility of using alternative methods for studying PFAS toxicity, offering insights into their dynamics and kinetics without animal testing. The strategy provides a promising framework for assessing other chemicals, advancing toxicology toward more human-relevant and ethical practices. • An integrated testing strategy was applied to assess PFAS immunotoxicity. • PFAS inhibited antibody production in human peripheral blood mononuclear cells. • In vitro distribution and PBK modelling supported QIVIVE. • Oral equivalent effect doses were comparable to EFSA guidance values. • The approach supports non-animal immunotoxicity risk assessment.
NX210c Demonstrates Therapeutic Potential to Restore Blood–Brain Barrier in a QSP Model of Relapsing–Remitting Multiple Sclerosis Giulia Russo, Fianne Sips, Simona Catozzi, Pauline Bambury, Annette Janus, Mario Torchia, Valentina Di Salvatore, Luca Emili, Daniel Röshammar, Francesco Pappalardo, Yann Godfrin International Journal of Molecular Sciences, 2026 Blood–brain barrier (BBB) breakdown is a hallmark of several neurological disorders, including multiple sclerosis (MS). NX210c, a novel therapeutic peptide, has shown promise in restoring BBB integrity, in both preclinical and clinical settings, offering potential for use in MS populations and across various central nervous system conditions with overlapping mechanisms. In this study, we evaluated the therapeutic potential of NX210c in patients with relapsing–remitting MS (RRMS) using a previous quantitative systems pharmacology (QSP) model currently redesigned to capture the dynamic interplay between BBB integrity and immune system activity. We validated the QSP model using both preclinical and clinical datasets, and generated virtual populations representing healthy individuals and RRMS patients for in silico testing. NX210c was assessed as both a monotherapy and in combination with established MS treatments. Simulations predicted time course changes in key BBB integrity markers, including tight junction protein (TJP) expression and transendothelial electrical resistance (TEER), under various dosing regimens. NX210c treatment was associated with a significant attenuation of BBB degradation compared to untreated controls (~7–8% higher TJP expression and BBB electrical resistance). Furthermore, we investigated the long-term impact of NX210c on clinical outcomes such as relapse rates. Both 5 and 10 mg/kg doses (single cycle [thrice-weekly for 4 weeks]) induced improvement in disease activity in RRMS patients, as well as a 10 mg/kg dose (single or repeated 4-week cycles every 6 months) in highly active patients. Particularly when administered alongside one of five commonly used MS therapies (interferon β-1a, teriflunomide, cladribine, natalizumab, ocrelizumab), in the highly active subpopulation, the model on average predicted a reduction in relapse frequency in the 10 mg NX210c-treated group versus untreated group from four to no relapses over two years. These findings suggest that NX210c may enhance therapeutic efficacy in RRMS by promoting BBB restoration and modulating immune responses, offering a promising avenue for combination treatment strategies.
Mechanistic insights into PFAS-induced effects on B lymphocyte activation and antibody secretion Martina Iulini, Karsten Beekmann, Ron L. A. P. Hoogenboom, Valentina Galbiati, Giulia Russo, Francesco Pappalardo, Styliani Fragki, Alicia Paini, Emanuela Corsini, Aafke W. F. Janssen Archives of Toxicology, 2026 Per- and polyfluoroalkyl substances (PFASs) are man-made organofluoride chemicals widely present in the environment, with exposure associated to various adverse health effects, including immunotoxicity. Recently, we showed that PFASs can directly impair antibody production, leading to decreased immunoglobulin (Ig) M and IgG release in human peripheral blood mononuclear cells (PBMCs) obtained from both male and female healthy donors. However, the underlying molecular mechanisms remain largely unknown. In this study, we aimed to address this gap by performing RNA sequencing to identify pathways and genes potentially involved in the observed immunotoxic effects. PBMCs were exposed to selected PFASs for 24 h, and to assess effects on antibody secretion, a subset was subsequently stimulated with CpG oligodeoxynucleotide ODN2006 and rhIL-2 for an additional six days. Transcriptomic analysis indicated activation of the glucocorticoid receptor (GR) and associated signaling pathways, supported by the upregulation of several GR-target genes and the prediction of glucocorticoids or the GR agonist dexamethasone as upstream regulators in Ingenuity Pathway Analysis. Moreover, the inhibitory effects of PFASs on antibody secretion were shown to be reversable by the GR antagonist Mifepristone, supporting the involvement of the GR in PFAS-mediated suppression of antibody secretion. Overall, this research advances our understanding of PFAS-induced immunotoxicity and identifies potential biomarkers for evaluating PFAS exposure and its associated health effects. Graphical abstract
Computational modeling of ATM signaling: a predictive framework for drug repurposing in ataxia-telangiectasia Aurora Eliana Merulla, Valentina Di Salvatore, Giorgia Serena Gullotta, Avisa Maleki, Giulia Russo, Filippo Caraci, Agata Copani, Francesco Pappalardo Npj Systems Biology and Applications, 2025 Ataxia-Telangiectasia (A-T) is a rare genetic disorder caused by ATM mutations, leading to impaired DNA repair, oxidative stress, and neurodegeneration. We developed a computational model of ATM-mediated signaling using ordinary differential equations in COPASI, capturing key processes including DNA damage sensing, cell cycle regulation, autophagy, and oxidative stress response. The model simulates physiological, ATM-deficient, and drug-treated conditions to explore repurposing strategies. We evaluated the effects of spermidine, omaveloxolone, and HDAC4 inhibition, revealing mechanisms by which these compounds modulate dysfunctional signaling. Sensitivity and stability analyses confirmed the model's robustness, while enrichment analysis validated involvement of key pathways. Our results highlight the synergistic potential of combining autophagy activation and epigenetic modulation to partially restore homeostasis in ATM-deficient cells. This work introduces a generalizable modeling framework for simulating disease-specific signaling dysfunction and identifying therapeutic interventions, illustrating the value of computational systems biology in rare disease drug repurposing.
Advancing the frontier of rare disease modeling: a critical appraisal of in silico technologies Francesca Pistollato, Fabia Furtmann, Lindsay J. Marshall, Surat Parvatam, Jan Turner, Flora Tshinanu Musuamba, Giulia Russo, Francesco Pappalardo Npj Digital Medicine, 2025 Rare diseases affect over 300 million people worldwide and pose unique research challenges. In silico approaches, such as mechanistic models, machine learning, and simulations, offer scalable tools for disease characterisation, drug discovery, and virtual trials. This review categorises these methods by context of use, critically appraises their strengths and limitations, and identifies barriers to translation, highlighting key opportunities and ongoing challenges in advancing computational strategies for rare disease research.
MSLesSeg: baseline and benchmarking of a new Multiple Sclerosis Lesion Segmentation dataset Francesco Guarnera, Alessia Rondinella, Elena Crispino, Giulia Russo, Clara Di Lorenzo, Davide Maimone, Francesco Pappalardo, Sebastiano Battiato Scientific Data, 2025 This paper presents MSLesSeg, a new, publicly accessible MRI dataset designed to advance research in Multiple Sclerosis (MS) lesion segmentation. The dataset comprises 115 scans of 75 patients including T1, T2 and FLAIR sequences, along with supplementary clinical data collected across different sources. Expert-validated annotations provide high-quality lesion segmentation labels, establishing a reliable human-labeled dataset for benchmarking. Part of the dataset was shared with expert scientists with the aim to compare the last automatic AI-based image segmentation solutions with an expert-biased handmade segmentation. In addition, an AI-based lesion segmentation of MSLesSeg was developed and technically validated against the last state-of-the-art methods. The dataset, the detailed analysis of researcher contributions, and the baseline results presented here mark a significant milestone for advancing automated MS lesion segmentation research.
In vitro approaches to investigate the effect of chemicals on antibody production: the case study of PFASs Martina Iulini, Valeria Bettinsoli, Ambra Maddalon, Valentina Galbiati, Aafke W. F. Janssen, Karsten Beekmann, Giulia Russo, Francesco Pappalardo, Styliani Fragki, Alicia Paini, Emanuela Corsini Archives of Toxicology, 2025 The increasing variety and quantity of new chemical substances have raised concerns about their potential immunotoxic effects, making it essential to assess their impact on human health. One key concern is the reduction of antibody production, as seen with per- and poly-fluoroalkyl substances (PFASs), commonly known as “forever chemicals.” Both in vivo and epidemiological data show that PFASs have immunosuppressive effects, leading to reduced antibody responses, particularly following vaccination. In animal studies, the T cell-dependent (TD) antibody response is the gold standard for assessing chemical effects on immune function. This study utilized two in vitro approaches to investigate the effects of chemicals on antibody production using human peripheral blood mononuclear cells. Initial tests used unstimulated, negative (vehicle), and positive (rapamycin) controls to confirm the robustness of the models. Subsequently, four long-chain PFASs (PFOA, PFOS, PFNA, and PFHxS) were tested. Keyhole limpet hemocyanin (KLH) was used to mimic the TD response, while a TLR9 agonist and IL-2 activated B cells for T cell-independent (TI) immunoglobulin production. The results demonstrated the ability to reproduce TD and TI responses in vitro with robust, reproducible outcomes across a cohort of 20 human donors. The data, consistent with existing literature, showed a significant reduction in anti-KLH IgM production, especially for PFOA in male donors. Similar trends were observed for all PFASs in suppressing total TI IgG and IgM production. These methods closely replicated in vivo conditions, offering a potential alternative to animal models in immunotoxicity assessments.
A computational framework for optimizing mRNA vaccine delivery via AI-guided nanoparticle design and in silico gene expression profiling Valentina Di Salvatore, Federica Cernuto, Giulia Russo, Francesco Pappalardo Frontiers in Immunology, 2025 Recent concerns about off-target immune activation following non-targeted mRNA vaccine delivery have prompted the need for rational design strategies that optimize nanoparticle formulations. Building upon our previous in silico work using the Universal Immune System Simulator to characterize immune responses to mRNA vaccines, we present a computational framework that integrates synthetic transcriptomics with artificial intelligence-driven optimization to guide the development of safer and more targeted lipid nanoparticles. We generated biologically informed, synthetic RNA-seq datasets to emulate gene expression profiles in immune-related tissues post-vaccination. Differential gene expression analysis identified compartment-specific transcriptional responses, which were then used to construct a risk index based on predicted immune activation and the number of upregulated immune markers. Parallelly, we trained a Random Forest regression model on simulated lipid nanoparticles formulations to predict immune activation values and embedded this model into a genetic algorithm to identify optimal lipid nanoparticles design parameters (size, charge, polyethylene glycol content, and targeting). The proposed framework enables early-stage, fully in silico screening of mRNA vaccine delivery strategies. Our results highlight the potential of combining mechanistic immune modeling, synthetic transcriptomic validation, and Artificial Intelligence-based design to accelerate the development of safer and more effective mRNA-based therapies. By enabling rapid, data-driven optimization of delivery systems prior to experimental validation, this approach can significantly shorten vaccine development timelines, reduce costs, and support the creation of more personalized and adaptable immunization strategies. In the long term, this paradigm shift toward computationally guided vaccine development could redefine the future of immunization, paving the way for next-generation vaccines that are safer, more targeted, and rapidly adaptable to emerging infectious threats and individual patient needs.
From Cellular Perturbation to Probabilistic Risk Assessments A. Maertens, Breanne Kincaid, Eric Bridgeford, Celine Brochot, Arthur de Carvalho E Silva, Jean‐Lou C. M. Dorne, L. Geris, T. Husøy, Nicole C. Kleinstreuer, Luiz Ladeira, Alistair Middleton, Joe Reynolds, Blanca Rodriguez, Erwin Roggen, Giulia Russo, Kris Thayer, Thomas Hartung Altex, 2025
Breaking the Cycle: Advancements in Universal Influenza Vaccine Design Valentina Di Salvatore, Elena Crispino, Giulia Russo, Francesco Pappalardo 2024 IEEE International Conference on Metrology for Extended Reality Artificial Intelligence and Neural Engineering Metroxraine 2024 Proceedings, 2024
Model Development Alexander Kulesza, Axel Loewe, Andrea Stenti, Chiara Nicolò, Enrique Morales-Orcajo, Eulalie Courcelles, Fianne Sips, Francesco Pappalardo, Giulia Russo, Marc Horner, Marco Viceconti, Martha De Cunha Maluf-Burgman, Raphaëlle Lesage, Steve Kreuzer Synthesis Lectures on Biomedical Engineering, 2024
Model Credibility Eulalie Courcelles, Marc Horner, Payman Afshari, Alexander Kulesza, Cristina Curreli, Cristina Vaghi, Enrique Morales-Orcajo, Francesco Pappalardo, Ghislain Maquer, Giulia Russo, Liesbet Geris, Marco Viceconti, Michael Neidlin, Philippe Favre, Raphaëlle Lesage, Steve Kreuzer, Vincenzo Carbone Synthesis Lectures on Biomedical Engineering, 2024
Toward A Regulatory Pathway for the Use of in Silico Trials in the CE Marking of Medical Devices F. Pappalardo, J. Wilkinson, F. Busquet, A. Bril, Mark Palmer, B. Walker, Cristina Curreli, G. Russo, Thierry Marchal, E. Toschi, R. Alessandrello, Vincenzo Costignola, I. Klingmann, Martina Contin, B. Staumont, M. Woiczinski, C. Kaddick, Valentina Di Salvatore, A. Aldieri, L. Geris, M. Viceconti IEEE Journal of Biomedical and Health Informatics, 2022
Text mining and word embedding for classification of decision making variables in breast cancer surgery G. Catanuto, N. Rocco, A. Maglia, P. Barry, A. Karakatsanis, G. Sgroi, G. Russo, F. Pappalardo, M.B. Nava, Joerg Heil, Andreas Karakatsanis, Walter Paul Weber, Eduardo Gonzalez, Abhishek Chatterjee, Cicero Urban, Malin Sund, Regis Resende Paulinelli, Christos Markopoulos, Isabel T. Rubio, Yazan A. Masannat, Francesco Meani, Chaitanyanand B. Koppiker, Chris Holcombe, John R. Benson, Jill R. Dietz, Melanie Walker, Zoltán Mátrai, Ayesha Shaukat, Bahadir Gulluoglu, Fabricio Brenelli, Florian Fitzal, Marco Mele, Tibor Kovacs European Journal of Surgical Oncology, 2022
Possible Contexts of Use for in Silico Trials Methodologies: A Consensus-Based Review Marco Viceconti, Luca Emili, Payman Afshari, Eulalie Courcelles, Cristina Curreli, Nele Famaey, Liesbet Geris, Marc Horner, Maria Cristina Jori, Alexander Kulesza, Axel Loewe, Michael Neidlin, Markus Reiterer, Cecile F. Rousseau, Giulia Russo, Simon J. Sonntag, Emmanuelle M. Voisin, Francesco Pappalardo IEEE Journal of Biomedical and Health Informatics, 2021
Scientific and regulatory evaluation of mechanistic in silico drug and disease models in drug development: Building model credibility Flora T. Musuamba, Ine Skottheim Rusten, Raphaëlle Lesage, Giulia Russo, Roberta Bursi, Luca Emili, Gaby Wangorsch, Efthymios Manolis, Kristin E. Karlsson, Alexander Kulesza, Eulalie Courcelles, Jean‐Pierre Boissel, Cécile F. Rousseau, Emmanuelle M. Voisin, Rossana Alessandrello, Nuno Curado, Enrico Dall’ara, Blanca Rodriguez, Francesco Pappalardo, Liesbet Geris Cpt Pharmacometrics and Systems Pharmacology, 2021
How can we accelerate COVID-19 vaccine discovery? Giulia Russo, Valentina Di Salvatore, Filippo Caraci, Cristina Curreli, Marco Viceconti, Francesco Pappalardo Expert Opinion on Drug Discovery, 2021
Verify: A toolbox for deterministic verification of computational models Giuseppe Alessandro Parasiliti Palumbo, Giulia Russo, Giuseppe Sgroi, Marco Viceconti, Marzio Pennisi, Cristina Curreli, Francesco Pappalardo Proceedings 2020 IEEE International Conference on Bioinformatics and Biomedicine Bibm 2020, 2020
Computational Immunogenetics Marta Gómez Perosanz, Giulia Russo, Jose Luis Sanchez-Trincado Lopez, Marzio Pennisi, Pedro A. Reche, Adrian Shepherd, Francesco Pappalardo Encyclopedia of Bioinformatics and Computational Biology Abc of Bioinformatics, 2019
GPU accelerated analysis of treg-teff cross regulation in relapsing-remitting multiple sclerosis Marco Beccuti, Paolo Cazzaniga, Marzio Pennisi, Daniela Besozzi, Marco S. Nobile, Simone Pernice, Giulia Russo, Andrea Tangherloni, Francesco Pappalardo Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2019
Computational immunogenetics Marta Gómez Perosanz, Giulia Russo, Jose Luis Sanchez-Trincado Lopez, Marzio Pennisi, Pedro A. Reche, Adrian Shepherd, Francesco Pappalardo Encyclopedia of Bioinformatics and Computational Biology Abc of Bioinformatics, 2018
The pathogenicity of immigration detention: a systemic conflict between medical ethics and harmful migration policies N Cocco, V Marchese, F Nicoli, G Russo, J Testa, A Corsaro, M Mazzetti The Lancet Regional Health–Europe 64 , 2026 2026
Mechanistic insights into PFAS-induced effects on B lymphocyte activation and antibody secretion M Iulini, K Beekmann, RLAP Hoogenboom, V Galbiati, G Russo, ... Archives of Toxicology, 1-18 , 2026 2026
Morphometric triage: Predicting postoperative quality of life after breast surgery using body measurements alone K Balafa, V Di Salvatore, G Russo, F Pappalardo, L Astorina, D Boland, ... European Journal of Cancer 237 , 2026 2026
Acceptability of tongue swabs for tuberculosis screening in migrant settings in northern Italy: A qualitative study R Codsi, F Saluzzo, RC Wood, AM Olson, G Russo, L Ragazzoni, ... PLOS Global Public Health 6 (3), e0004908 , 2026 2026 Citations: 1
Local PI(4,5)P 2 synthesis by septin-associated PIPKIγ isoforms controls centralspindlin association with the midbody during cytokinesis G Russo, N Hümpfer, N Jaensch, S Restel, C Schmied, F Heyd, ... Nature Communications 17 (1), 1482 , 2026 2026 Citations: 1
NX210c Demonstrates Therapeutic Potential to Restore Blood–Brain Barrier in a QSP Model of Relapsing–Remitting Multiple Sclerosis G Russo, F Sips, S Catozzi, P Bambury, A Janus, M Torchia, ... International Journal of Molecular Sciences 27 (3), 1349 , 2026 2026
An integrated in vitro and in silico testing strategy applied to PFAS inhibition of antibody production to define a tolerable daily intake M Iulini, AWF Janssen, K Beekmann, G Russo, F Pappalardo, S Fragki, ... Toxicology Letters, 111817 , 2026 2026 Citations: 1
A computational framework for optimizing mRNA vaccine delivery via AI-guided nanoparticle design and in silico gene expression profiling V Di Salvatore, F Cernuto, G Russo, F Pappalardo Frontiers in Immunology 16, 1628583 , 2025 2025 Citations: 4
In-silico epitope-based vaccines design: progress, challenges and the road ahead F Cernuto, A Maleki, G Russo, V Di Salvatore, F Pappalardo Expert Opinion on Drug Discovery 20 (12), 1701-1712 , 2025 2025 Citations: 6
Predictive modeling of postoperative patient-reported outcomes after breast surgery using breast-Q scores: a machine learning approach R Gioco, V Di Salvatore, K Balafa, D Boland, I Cannata, F Caruso, ... European Journal of Surgical Oncology 51 , 2025 2025
Computational modeling of ATM signaling: a predictive framework for drug repurposing in ataxia-telangiectasia AE Merulla, V Di Salvatore, GS Gullotta, A Maleki, G Russo, F Caraci, ... npj Systems Biology and Applications , 2025 2025
Advancing the frontier of rare disease modeling: a critical appraisal of in silico technologies F Pistollato, F Furtmann, LJ Marshall, S Parvatam, J Turner, ... npj Digital Medicine 8 (1), 676 , 2025 2025 Citations: 2
Agent-Based Modeling: An Evolving Paradigm for Complex Biological Systems G Russo, F Pappalardo Bioinformatics-Recent Advances: Recent Advances, 87 , 2025 2025
A Comparative Evaluation of Diffusion Based Networks for Multiple Sclerosis Lesion Segmentation A Rondinella, F Guarnera, A Ortis, E Crispino, G Russo, F Pappalardo, ... International Conference on Image Analysis and Processing, 405-416 , 2025 2025
An uncommon case of neonatal asphyxia associated with infantile-onset Pompe disease F Leo, L Barchi, G Russo, E Balestri, E Chesi, F Di Dio, L Garavelli, ... Italian Journal of Pediatrics 51 (1), 260 , 2025 2025
Global blind spots in TB control-the need to focus on deprived urban neighbourhoods D Zenner, O Oyebode, G Russo, F Samuels, Z Seisay, D Banze, ... The international journal of tuberculosis and lung disease: the official … , 2025 2025
Four months daily rifampicin vs. 3 months daily rifampicin/isoniazid for the treatment of tuberculosis infection in asylum seekers: a randomized controlled trial A Matteelli, G Russo, L Rossi, C Cerini, C Cimaglia, B Formenti, ... Clinical Microbiology and Infection 31 (8), 1330-1335 , 2025 2025 Citations: 2
MSLesSeg: baseline and benchmarking of a new Multiple Sclerosis Lesion Segmentation dataset F Guarnera, A Rondinella, E Crispino, G Russo, C Di Lorenzo, ... Scientific Data 12 (1), 920 , 2025 2025 Citations: 25
When Bioinformatics Meets Agent-Based Modeling: An Evolving Paradigm for Complex Biological Systems G Russo, F Pappalardo Bioinformatics-Recent Advances , 2025 2025
In vitro approaches to investigate the effect of chemicals on antibody production: the case study of PFASs M Iulini, V Bettinsoli, A Maddalon, V Galbiati, AWF Janssen, K Beekmann, ... Archives of toxicology 99 (5), 2075-2086 , 2025 2025 Citations: 9
MOST CITED SCHOLAR PUBLICATIONS
In silico clinical trials: concepts and early adoptions F Pappalardo, G Russo, FM Tshinanu, M Viceconti Briefings in bioinformatics 20 (5), 1699-1708 , 2019 2019 Citations: 347
First evaluation of QuantiFERON-TB Gold Plus performance in contact screening L Barcellini, E Borroni, J Brown, E Brunetti, D Campisi, PF Castellotti, ... European respiratory journal 48 (5), 1411-1419 , 2016 2016 Citations: 203
Scientific and regulatory evaluation of mechanistic in silico drug and disease models in drug development: Building model credibility FT Musuamba, I Skottheim Rusten, R Lesage, G Russo, R Bursi, L Emili, ... CPT: pharmacometrics & systems pharmacology 10 (8), 804-825 , 2021 2021 Citations: 175
Mutations in disordered regions can cause disease by creating dileucine motifs K Meyer, M Kirchner, B Uyar, JY Cheng, G Russo, ... Cell 175 (1), 239-253. e17 , 2018 2018 Citations: 168
Agent‐based modeling of the immune system: NetLogo, a promising framework F Chiacchio, M Pennisi, G Russo, S Motta, F Pappalardo BioMed research international 2014 (1), 907171 , 2014 2014 Citations: 158
The combination of artificial intelligence and systems biology for intelligent vaccine design G Russo, P Reche, M Pennisi, F Pappalardo Expert Opinion on Drug Discovery 15 (11), 1267-1281 , 2020 2020 Citations: 93
In silico design of recombinant multi-epitope vaccine against influenza A virus A Maleki, G Russo, GA Parasiliti Palumbo, F Pappalardo BMC bioinformatics 22 (Suppl 14), 617 , 2021 2021 Citations: 88
Modeling biology spanning different scales: an open challenge F Castiglione, F Pappalardo, C Bianca, G Russo, S Motta BioMed research international 2014 (1), 902545 , 2014 2014 Citations: 86
Computational modeling of PI3K/AKT and MAPK signaling pathways in melanoma cancer F Pappalardo, G Russo, S Candido, M Pennisi, S Cavalieri, S Motta, ... PLoS One 11 (3), e0152104 , 2016 2016 Citations: 85
Immune-checkpoint inhibitors from cancer to COVID-19: A promising avenue for the treatment of patients with COVID-19 S Vivarelli, L Falzone, F Torino, G Scandurra, G Russo, R Bordonaro, ... International journal of oncology 58 (2), 145-157 , 2020 2020 Citations: 80
Boosting multiple sclerosis lesion segmentation through attention mechanism A Rondinella, E Crispino, F Guarnera, O Giudice, A Ortis, G Russo, ... Computers in Biology and Medicine 161, 107021 , 2023 2023 Citations: 78
Credibility of In Silico Trial Technologies—A Theoretical Framing M Viceconti, MA Juárez, C Curreli, M Pennisi, G Russo, F Pappalardo IEEE journal of biomedical and health informatics 24 (1), 4-13 , 2019 2019 Citations: 78
Tamoxifen therapy in a murine model of myotubular myopathy N Maani, N Sabha, K Rezai, A Ramani, L Groom, N Eltayeb, ... Nature Communications 9 (1), 4849 , 2018 2018 Citations: 75
Possible Contexts of Use for In Silico Trials Methodologies: A Consensus-Based Review M Viceconti, L Emili, P Afshari, E Courcelles, C Curreli, N Famaey, L Geris, ... IEEE Journal of Biomedical and Health Informatics 25 (10), 3977-3982 , 2021 2021 Citations: 71
In silico trial to test COVID-19 candidate vaccines: a case study with UISS platform G Russo, M Pennisi, E Fichera, S Motta, G Raciti, M Viceconti, ... BMC bioinformatics 21 (Suppl 17), 527 , 2020 2020 Citations: 66
Wild boars’ social structure in the Mediterranean habitat V Maselli, D Rippa, G Russo, R Ligrone, O Soppelsa, B D’Aniello, P Raia, ... Italian Journal of Zoology 81 (4), 610-617 , 2014 2014 Citations: 63
The potential of computational modeling to predict disease course and treatment response in patients with relapsing multiple sclerosis F Pappalardo, G Russo, M Pennisi, GA Parasiliti Palumbo, G Sgroi, ... Cells 9 (3), 586 , 2020 2020 Citations: 53
Computational modelling approaches to vaccinology F Pappalardo, D Flower, G Russo, M Pennisi, S Motta Pharmacological research 92, 40-45 , 2015 2015 Citations: 52
A computational model to predict the immune system activation by citrus-derived vaccine adjuvants F Pappalardo, E Fichera, N Paparone, A Lombardo, M Pennisi, G Russo, ... Bioinformatics 32 (17), 2672-2680 , 2016 2016 Citations: 49
Examining the pre-war health burden of Ukraine for prioritisation by European countries receiving Ukrainian refugees V Marchese, B Formenti, N Cocco, G Russo, J Testa, F Castelli, ... The Lancet Regional Health–Europe 15 , 2022 2022 Citations: 47
