2024 Publications
- Predictive Modelling of Allergic Responses to Chemical Sensitizers: Distinguishing Skin and Respiratory Reactions: https://ieeexplore.ieee.org/document/10385284
- Genetic Algorithm-Based Prediction of Emerging SARS-CoV-2 Variants: A Computational Biology Perspective: https://ieeexplore.ieee.org/document/10385446
- Modeling Cutaneous Leishmaniasis: Insights into M1/M2 Macrophage Dynamics Using the Universal Immune System Simulator: https://ieeexplore.ieee.org/document/10385555
- Machine Learning techniques application to predict the quality of life degree in breast cancer patients: https://ieeexplore.ieee.org/document/10385705
- Modeling and Simulation of anakinra effects on severe COVID19 Patients: https://ieeexplore.ieee.org/document/10385252
- Enhancing Multiple Sclerosis Lesion Segmentation in Multimodal MRI Scans with Diffusion Models: https://ieeexplore.ieee.org/document/10385334
- Universal Immune System Simulator Analysis: The Impact of the Variable Approach Technique® on Enhancing Immune Defense Against infectious diseases: https://ieeexplore.ieee.org/document/10385460
2023 Publications
- Beyond the state of the art of reverse vaccinology: predicting vaccine efficacy with the universal immune system simulator for influenza: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-023-05374-1
- Boosting multiple sclerosis lesion segmentation through attention mechanism: https://www.sciencedirect.com/science/article/pii/S0010482523004869
- Moving Toward a Question-Centric Approach for Regulatory Decision Making in the Context of Drug Assessment: https://pubmed.ncbi.nlm.nih.gov/36708100/
- Natural Language Processing to Extract Meaningful Information from a Corpus of Written Knowledge in Breast Cancer: Transforming Books into Data: https://karger.com/brc/article/18/3/209/843776/Natural-Language-Processing-to-Extract-Meaningful
- Artificial intelligence and real-world data for drug and food safety – A regulatory science perspective: https://www.sciencedirect.com/science/article/pii/S0273230023000569
- Computational identification of differentially-expressed genes as suggested novel COVID-19 biomarkers: A bioinformatics analysis of expression profiles: https://www.sciencedirect.com/science/article/pii/S2001037023002192
- Reverse Vaccinology for Influenza A Virus: From Genome Sequencing to Vaccine Design: https://pubmed.ncbi.nlm.nih.gov/37258929/
- Moving forward through the in silico modeling of multiple sclerosis: Treatment layer implementation and validation: https://pubmed.ncbi.nlm.nih.gov/37266405/
- A Credibility Assessment Plan for an In Silico Model that Predicts the Dose-Response Relationship of New Tuberculosis Treatments: https://pubmed.ncbi.nlm.nih.gov/36115895/
2022 Publications
- A multi-step and multi-scale bioinformatic protocol to investigate potential SARS-CoV-2 vaccine targets: https://pubmed.ncbi.nlm.nih.gov/34607353/
- Text mining and word embedding for classification of decision making variables in breast cancer surgery: https://pubmed.ncbi.nlm.nih.gov/35410759/
- Translatability and transferability of in silico models: Context of use switching to predict the effects of environmental chemicals on the immune system: https://pubmed.ncbi.nlm.nih.gov/35495116/
- Computational modelling and simulation for immunotoxicity prediction induced by skin sensitisers: https://www.sciencedirect.com/science/article/pii/S2001037022004810
- In silico clinical trials for relapsing-remitting multiple sclerosis with MS TreatSim: https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-022-02034-x
- Toward A Regulatory Pathway for the Use of in Silico Trials in the CE Marking of Medical Devices: https://pubmed.ncbi.nlm.nih.gov/35951559/
- In silico clinical trials for relapsing-remitting multiple sclerosis with MS TreatSim: https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-022-02034-x
2021 Publications
- Quality by design tools reducing the gap from bench to bedside for nanomedicine: https://www.sciencedirect.com/science/article/abs/pii/S0939641121002605?via%3Dihub
- Bayesian Augmented Clinical Trials in TB Therapeutic Vaccination: https://www.frontiersin.org/articles/10.3389/fmedt.2021.719380/full
- A multi-step and multi-scale bioinformatic protocol to investigate potential SARS-CoV-2 vaccine targets: https://academic.oup.com/bib/article/23/1/bbab403/6381250?login=true
- PEAK: A Clever Python Tool for Exploratory, Regression, and Classification Data. A Case Study for COVID-19: https://link.springer.com/chapter/10.1007/978-3-030-88163-4_31
- Scientific and regulatory evaluation of mechanistic in silico drug and disease models in drug development: Building model credibility: https://ascpt.onlinelibrary.wiley.com/doi/10.1002/psp4.12669
- Possible Contexts of Use for In Silico Trials Methodologies: A Consensus-Based Review: https://ieeexplore.ieee.org/document/9462824
- Verification of an agent-based disease model of human Mycobacterium tuberculosis infection: http://Verification of an agent-based disease model of human Mycobacterium tuberculosis infection
- How can we accelerate COVID-19 vaccine discovery?: https://www.tandfonline.com/doi/full/10.1080/17460441.2021.1935861
- In silico trials: Verification, validation and uncertainty quantification of predictive models used in the regulatory evaluation of biomedical products: https://www.sciencedirect.com/science/article/pii/S1046202319302452
- Immune-checkpoint inhibitors from cancer to COVID‑19: A promising avenue for the treatment of patients with COVID‑19: https://pubmed.ncbi.nlm.nih.gov/33491759/
- Uncertainty quantification and sensitivity analysis for in silico trial platform: a preliminary application on UISS-MS: https://ieeexplore.ieee.org/document/9669331
2020 Publications
- Credibility of In Silico Trial Technologies—A Theoretical Framing: https://ieeexplore.ieee.org/document/8884189
- Gene Silencing of Transferrin-1 Receptor as a Potential Therapeutic Target for Human Follicular and Anaplastic Thyroid Cancer: https://www.sciencedirect.com/science/article/pii/S2372770520300097?via%3Dihub
- The Potential of Computational Modeling to Predict Disease Course and Treatment Response in Patients with Relapsing Multiple Sclerosis: https://www.mdpi.com/2073-4409/9/3/586
- The combination of artificial intelligence and systems biology for intelligent vaccine design: https://www.tandfonline.com/doi/full/10.1080/17460441.2020.1791076
- Efficient and Settings-Free Calibration of Detailed Kinetic Metabolic Models with Enzyme Isoforms Characterization: https://link.springer.com/chapter/10.1007/978-3-030-34585-3_17
- Multiple Sclerosis Disease: A Computational Approach for Investigating Its Drug Interactions: https://link.springer.com/chapter/10.1007/978-3-030-63061-4_26
- In Silico Evaluation of Daclizumab and Vitamin D Effects in Multiple Sclerosis Using Agent Based Models: https://link.springer.com/chapter/10.1007/978-3-030-63061-4_25
- Verify: a toolbox for deterministic verification of computational models: https://ieeexplore.ieee.org/document/9313312
- Modeling, simulation and prediction of protein structures for the design of oncolytic viruses: https://ieeexplore.ieee.org/document/9313198
- Evaluation of the predictive capability of PETAL tool: a retrospective study on potential tyrosine kinases drug resistance targets: https://ieeexplore.ieee.org/document/9312986
- PETAL: a Python tool for deep analysis of biological pathways: https://academic.oup.com/bioinformatics/article/36/22-23/5553/6039123?login=true
- Toward computational modelling on immune system function: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03897-5
- Generation of digital patients for the simulation of tuberculosis with UISS-TB: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03776-z
- Moving forward through the in silico modeling of tuberculosis: a further step with UISS-TB: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03762-5
- Computational modeling of the immune response in multiple sclerosis using epimod framework: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03823-9
- In silico trial to test COVID-19 candidate vaccines: a case study with UISS platform: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03872-0
- Immune-checkpoint inhibitors from cancer to COVID‑19: A promising avenue for the treatment of patients with COVID‑19 (Review): https://www.spandidos-publications.com/10.3892/ijo.2020.5159