MEDICA Project

@article{filogna2026machine,

title = {Machine Learning to Identify a New Digital Biomarker to Monitor Everyday Upper Limb Use in Children with Unilateral Cerebral Palsy},

author = {Silvia Filogna and Giuseppe Prencipe and Alina Sîrbu and Elena Beani and Davide Marchi and Giordano Scerra and Giuseppina Sgandurra},

year  = {2026},

date = {2026-01-01},

urldate = {2026-01-01},

journal = {Machine Learning},

volume = {115},

number = {2},

pages = {25},

publisher = {Springer},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{massafra2026machine,

title = {Machine Learning for analysis of Multiple Sclerosis cross-tissue bulk and single-cell transcriptomics data},

author = {Francesco Massafra and Samuele Punzo and Silvia Giulia Galfré and Alessandro Maglione and Simone Pernice and Stefano Forti and Simona Rolla and Marco Beccuti and Marinella Clerico and Corrado Priami and Alina Sîrbu},

year  = {2026},

date = {2026-01-01},

urldate = {2026-01-01},

journal = {arXiv preprint arXiv:2603.05572},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pham2026knowledge,

title = {Knowledge Graph Extraction from Biomedical Literature for Alkaptonuria Rare Disease},

author = {Giang Pham and Rebecca Finetti and Caterina Graziani and Bianca Roncaglia and Asma Bendjeddou and Linda Brodo and Sara Brunetti and Moreno Falaschi and Stefano Forti and Silvia Giulia Galfré and Paolo Milazzo and Corrado Priami and Annalisa Santucci and Ottavia Spiga and Alina Sîrbu.},

year  = {2026},

date = {2026-01-01},

urldate = {2026-01-01},

journal = {arXiv preprint arXiv:2603.15711},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Castiglioni2025,

title = {DT-Stark: a tool for evaluating the effectiveness of digital twins through feedback and perturbations},

author = {Valentina Castiglioni and Ruggero Lanotte and Michele Loreti and Simone Tini},

doi = {10.1007/s10009-025-00826-w},

issn = {1433-2787},

year  = {2025},

date = {2025-10-00},

urldate = {2025-10-00},

journal = {Int J Softw Tools Technol Transfer},

volume = {27},

number = {5},

pages = {443--464},

publisher = {Springer Science and Business Media LLC},

abstract = {<jats:title>Abstract</jats:title>

                  <jats:p>

                    A

                    <jats:italic>digital twin</jats:italic>

                    is a virtual replica of a physical system that has to interact with it in real-time in order to facilitate decision-making, to reduce failures and costs, and to ensure a coherent and safe system execution. We call

                    <jats:italic>effectiveness</jats:italic>

                    the ability of the digital twin to direct the physical counterpart. In this paper we provide the means to evaluate the effectiveness of a digital twin in the case that the physical system is operating under uncertainty, and it is therefore subject to

                    <jats:italic>perturbations</jats:italic>

                    . Specifically, we present the

                    <jats:sc>DT-Stark</jats:sc>

                    tool, that extends

                    <jats:sc>Stark</jats:sc>

                    , a tool for modelling and verification of systems operating under uncertainty, with

                    <jats:italic>feedback</jats:italic>

                    , a special mechanism that allow us to model the communications, and their effects, between the digital and the physical (perturbed) twin in a concise, clean fashion. We can then exploit the features of

                    <jats:sc>Stark</jats:sc>

                    to compare the behaviour of the twins, to verify properties over them, and to measure effectiveness. We provide some examples of the use of our tool by applying it to the evaluation of the effectiveness of digital twins in two robotic scenarios: an industrial plant and a smart hospital.

                  </jats:p>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1093/bioinformatics/btaf103,

title = {UnifiedGreatMod: a new holistic modelling paradigm for studying biological systems on a complete and harmonious scale},

author = {Riccardo Aucello and Simone Pernice and Dora Tortarolo and Raffaele A Calogero and Celia Herrera-Rincon and Giulia Ronchi and Stefano Geuna and Francesca Cordero and Pietro Lió and Marco Beccuti},

url = {https://doi.org/10.1093/bioinformatics/btaf103},

doi = {10.1093/bioinformatics/btaf103},

issn = {1367-4811},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Bioinformatics},

volume = {41},

number = {3},

pages = {btaf103},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{brodo2025slicing,

title = {Slicing analyses for negative dependencies in reaction systems modeling gene regulatory networks},

author = {Linda Brodo and Roberto Bruni and Moreno Falaschi and Roberta Gori and Paolo Milazzo},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Natural Computing},

volume = {24},

number = {4},

pages = {1045–1074},

publisher = {Springer},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{brodo2025simulation,

title = {Simulation and Analysis of Distributed Reaction Systems},

author = {Linda Brodo and Roberto Bruni and Moreno Falaschi and Ion Petre},

url = {https://ieeexplore.ieee.org/abstract/document/11071694/},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {IEEE Access},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{PHAM2025100185,

title = {A comprehensive review of the use of Shapley value to assess node importance in the analysis of biological networks},

author = {Giang Pham and Paolo Milazzo},

url = {https://www.sciencedirect.com/science/article/pii/S2666990025000096},

doi = {https://doi.org/10.1016/j.cmpbup.2025.100185},

issn = {2666-9900},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Computer Methods and Programs in Biomedicine Update},

volume = {7},

pages = {100185},

abstract = {Background: 

In 2017, Lundberg and Lee introduced SHAP, a breakthrough in Explainable AI, creatively applying the Shapley value to estimate the importance of input features in machine learning outputs. The Shapley value, from cooperative game theory, fairly distributes system gains among participants. Inspired by SHAP’s success, this survey explores the application of Shapley value-based methods in biological network analysis. 

Method: 

We conducted a comprehensive literature search on the application of the Shapley value in biological network analysis from 2004 to 2024. From this, we focused on studies that applied the Shapley value in innovative and non-trivial ways, distinct from its typical usage. 

Result: 

The review identified six original studies that provide novel applications of the Shapley value in analyzing biological networks. These methods have also inspired further development and applications. For each, we discuss the foundational contributions, subsequent advancements, and applications. 

Discussion: 

Although the reviewed methods share the common objective of using the Shapley value to estimate an element’s contribution within a system, each one takes a distinct approach to modeling the cooperative game. Some methods employ game settings that enable more efficient Shapley value calculations, albeit with a narrower scope, as they are tailored to specific problems. Other methods offer broader applicability but encounter the usual computational challenges associated with calculating the exact Shapley value due to its time complexity. Fortunately, these challenges can be mitigated through the use of approximation techniques. Despite the computational challenges, Shapley value-based methods demonstrate to be beneficial for the interpretation of biological networks.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{borghini2025mieo,

title = {MIEO: encoding clinical data to enhance cardiovascular event prediction},

author = {Davide Borghini and Davide Marchi and Angelo Nardone and Giordano Scerra and Silvia Giulia Galfrè and Alessandro Pingitore and Giuseppe Prencipe and Corrado Priami and Alina Sîrbu},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {arXiv preprint arXiv:2510.11257},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{punzo2025machine,

title = {A Machine Learning Pipeline for Multiple Sclerosis Biomarker Discovery: Comparing explainable AI and Traditional Statistical Approaches},

author = {Samuele Punzo and Silvia Giulia Galfrè and Francesco Massafra and Alessandro Maglione and Corrado Priami and Alina Sîrbu},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {arXiv preprint arXiv:2509.22484},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{BBF24,

title = {A framework for monitored dynamic slicing of Reaction Systems},

author = {Linda Brodo and Roberto Bruni and Moreno Falaschi},

doi = {https://doi.org/10.1007/s11047-024-09976-3},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Natural Computing},

abstract = {Reaction systems (RSs) are a computational framework inspired by biochemical mechanisms. A RS defines a finite set of reactions over a finite set of entities. Typically each reaction has a local scope, because it is concerned with a small set of entities, but complex models can involve a large number of reactions and entities, and their computation can manifest unforeseen emerging behaviours. When a deviation is detected, like the unexpected production of some entities, it is often difficult to establish its causes, e.g., which entities were directly responsible or if some reaction was misconceived. Slicing is a well-known technique for debugging, which can point out the program lines containing the faulty code. In this paper, we define the first dynamic slicer for RSs and show that it can help to detect the causes of erroneous behaviour and highlight the involved reactions for a closer inspection. To fully automate the debugging process, we propose to distil monitors for starting the slicing whenever a violation from a safety specification is detected. We have integrated our slicer in BioResolve, written in Prolog which provides many useful features for the formal analysis of RSs. We define the slicing algorithm for basic RSs and then enhance it for dealing with quantitative extensions of RSs, where timed processes and linear processes can be represented. Our framework is shown at work on suitable biologically inspired RS models.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{CASTIGLIONI2024114934,

title = {Robustness for biochemical networks: Step-by-step approach},

author = {Valentina Castiglioni and Ruggero Lanotte and Michele Loreti and Desiree Manicardi and Simone Tini},

url = {https://www.sciencedirect.com/science/article/pii/S0304397524005516},

doi = {https://doi.org/10.1016/j.tcs.2024.114934},

issn = {0304-3975},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Theoretical Computer Science},

volume = {1022},

pages = {114934},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{brodo2024causal,

title = {Causal analysis of positive Reaction Systems},

author = {Linda Brodo and Roberto Bruni and Moreno Falaschi and Roberta Gori and Paolo Milazzo and Valeria Montagna and Pasquale Pulieri},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {International Journal on Software Tools for Technology Transfer},

pages = {1–18},

publisher = {Springer},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{bruni2024melding,

title = {Melding Boolean networks and reaction systems under synchronous, asynchronous and most permissive semantics},

author = {Roberto Bruni and Roberta Gori and Paolo Milazzo and Hélène Siboulet},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Natural Computing},

volume = {23},

number = {2},

pages = {235–267},

publisher = {Springer},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{BBFO24,

title = {ccReact: a rewriting framework for the formal analysis of reaction systems},

author = {Demis Ballis and Linda Brodo and Moreno Falaschi and Carlos Olarte},

url = {https://doi.org/10.1007/s10009-024-00772-z},

doi = {10.1007/s10009-024-00772-z},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {International Journal on Software Tools for Technology Transfer},

volume = {26},

pages = {707–725},

publisher = {Springer},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{BBF24b,

title = {Modeling and Analyzing Reaction Systems in Maude},

author = {Demis Ballis and Linda Brodo and Moreno Falaschi},

url = {https://doi.org/10.3390/electronics13061139},

doi = {10.3390/electronics13061139},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Electronics},

volume = {13},

number = {6},

publisher = {MDPI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/nc/BrodoBF24,

title = {A Computational Model of the Secondary Hemostasis Pathway in Reaction Systems},

author = {Asma Bendjeddou and Linda Brodo and Moreno Falaschi and Elisa Benedetta Primavera Tiezzi},

url = {https://doi.org/10.3390/math12152422},

doi = {10.3390/math12152422},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Mathematics},

volume = {12},

number = {15},

publisher = {MDPI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{PERNICE2023104546,

title = {A new computational workflow to guide personalized drug therapy},

author = {Simone Pernice and Alessandro Maglione and Dora Tortarolo and Roberta Sirovich and Marinella Clerico and Simona Rolla and Marco Beccuti and Francesca Cordero},

url = {https://www.sciencedirect.com/science/article/pii/S1532046423002678},

doi = {https://doi.org/10.1016/j.jbi.2023.104546},

issn = {1532-0464},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Journal of Biomedical Informatics},

volume = {148},

pages = {104546},

abstract = {Objective: 

Computational models are at the forefront of the pursuit of personalized medicine thanks to their descriptive and predictive abilities. In the presence of complex and heterogeneous data, patient stratification is a prerequisite for effective precision medicine, since disease development is often driven by individual variability and unpredictable environmental events. Herein, we present GreatNectorworkflow as a valuable tool for (i) the analysis and clustering of patient-derived longitudinal data, and (ii) the simulation of the resulting model of patient-specific disease dynamics. 

Methods: 

GreatNectoris designed by combining an analytic strategy composed of CONNECTOR, a data-driven framework for the inspection of longitudinal data, and an unsupervised methodology to stratify the subjects with GreatMod, a quantitative modeling framework based on the Petri Net formalism and its generalizations. 

Results: 

To illustrate GreatNectorcapabilities, we exploited longitudinal data of four immune cell populations collected from Multiple Sclerosis patients. Our main results report that the T-cell dynamics after alemtuzumab treatment separate non-responders versus responders patients, and the patients in the non-responders group are characterized by an increase of the Th17 concentration around 36 months. 

Conclusion: 

GreatNectoranalysis was able to stratify individual patients into three model meta-patients whose dynamics suggested insight into patient-tailored interventions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}