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• Curreli, C., Di Salvatore, V., Russo, G., Pappalardo, F., & Viceconti, M. (2022). A Credibility Assessment Plan for an In Silico Model that Predicts the Dose–Response Relationship of New Tuberculosis Treatments. Annals of Biomedical Engineering, 1-11

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• Kiagias, D., Russo, G., Sgroi, G., Pappalardo, F. and Miguel A. Juárez, Bayesian Augmented Clinical Trials in TB Therapeutic Vaccination, Frontiers in medical technologies (2021).

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• Russo, G., Di Salvatore, V., Sgroi, G., Parasiliti Palumbo, G.A., Pappalardo, F., A multi-step and multi-scale bioinformatic protocol to investigate potential SARS-CoV-2 vaccine targets, Briefing of Bioinformatics, 2021.

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• Russo, G., Pennisi, M., Fichera, E. et al. In silico trial to test COVID-19 candidate vaccines: a case study with UISS platform. BMC Bioinformatics 21, 527 (2020).
   

• Juárez, M.A., Pennisi, M., Russo, G. et al. Generation of digital patients for the simulation of tuberculosis with UISS-TB. BMC Bioinformatics 21, 449 (2020).
   

• Russo, G., Sgroi, G., Parasiliti Palumbo, G.A. et al. Moving forward through the in silico modeling of tuberculosis: a further step with UISS-TB. BMC Bioinformatics 21, 458 (2020).
   

• ​​M. Viceconti, F. Pappalardo, B. Rodriguez, M. Horner, J. Bischoff, F. Musuamba Tshinanu. In Silico Trials: Verification, Validation And Uncertainty Quantification Of Predictive Models Used In The Regulatory Evaluation Of Biomedical Products. Methods. 2020 Jan 25. pii: S1046-2023(19)30245-2.

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• M. Pennisi, M. A. Juarez, G. Russo, F. Pappalardo, and M. Viceconti. Generation of digital patients for the simulation of tuberculosis with UISS-TB. In 3rd International Workshop on Computational Methods for the Immune System Function (CMISF 2019), IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2019), pp. 2163–2167, 2019.

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• G. Russo, F. Pappalardo, M. A. Juarez, M. Pennisi, P. J. Cardona, R. Colre, E. Fichera, M. Viceconti, Evaluation of the efficacy of RUTI and ID93/GLA-SE vaccines in tuberculosis treatment: in silico trial through UISS-TB simulator. 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, CA, USA, 2019, pp. 2197-2201

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• M. K. Chimeh, P. Heywood, M. Pennisi, F. Pappalardo and P. Richmond. Parallelisation strategies for agent based simulation of immune systems. BMC Bioinformatics 2019, 20(Suppl 6):579

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• M. Pennisi, G. Russo, G. Sgroi, A. Bonaccorso, G. A. Parasiliti Palumbo, E. Fichera, D. K. Mitra, K. B. Walker, P. Cardona, M. Amat, M. Viceconti and F. Pappalardo. Predicting the artificial immunity induced by RUTI® vaccine against tuberculosis using universal immune system simulator (UISS). BMC Bioinformatics 2019, 20(Suppl 6):504

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• Viceconti, M., Juárez, M. A., Curreli, C., Pennisi, M., Russo, G., & Pappalardo, F. (2019). Credibility of In Silico Trial Technologies—A Theoretical Framing. IEEE journal of biomedical and health informatics, 24(1), 4-13.

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• F. Pappalardo, G. Russo, F. M. Tshinanu, and M. Viceconti. In silico clinical trials: concepts and early adoptions. Briefings in Bioinformatics, 2018.

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• ​F. Pappalardo, G. Russo, M. Pennisi, G. Sgroi, G. A. Parasiliti Palumbo, S. Motta, and E. Fichera. An agent based modeling approach for the analysis of tuberculosis - immune system dynamics. In 2nd International Workshop on Computational Methods for the Immune System Function (CMISF 2018), IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2018), pages 1386–1392, 2018.