MEDICA Project

@inproceedings{pham2025preliminary,

title = {Gene Importance Assessment based on Shapley Values for Boolean Networks: Validation and Scalability Analysis},

author = {Giang Pham and Paolo Milazzo},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

booktitle = {International Symposium ``from Data to Models and Back'' (DataMod 2024)},

volume = {LNCS 15556},

organization = {Springer},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{graziani2024the,

title = {The Expressive Power of Path-Based Graph Neural Networks},

author = {Caterina Graziani and Tamara Drucks and Fabian Jogl and Monica Bianchini and scarselli and Thomas Gärtner},

url = {https://openreview.net/forum?id=io1XSRtcO8},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {Forty-first International Conference on Machine Learning},

abstract = {We systematically investigate the expressive power of path-based graph neural networks. While it has been shown that path-based graph neural networks can achieve strong empirical results, an investigation into their expressive power is lacking. Therefore, we propose PATH-WL, a general class of color refinement algorithms based on paths and shortest path distance information. We show that PATH-WL is incomparable to a wide range of expressive graph neural networks, can count cycles, and achieves strong empirical results on the notoriously difficult family of strongly regular graphs. Our theoretical results indicate that PATH-WL forms a new hierarchy of highly expressive graph neural networks.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/cmsb/CastiglioniLT24,

title = {Bio-Stark: A Tool for the Time-Point Robustness Analysis of Biological  Systems},

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

editor = {Roberta Gori and Paolo Milazzo and Mirco Tribastone},

url = {https://doi.org/10.1007/978-3-031-71671-3_5},

doi = {10.1007/978-3-031-71671-3_5},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {Computational Methods in Systems Biology - 22nd International Conference, 

 CMSB 2024, Pisa, Italy, September 16-18, 2024, Proceedings},

volume = {14971},

pages = {62–70},

publisher = {Springer},

series = {Lecture Notes in Computer Science},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/concur/CastiglioniLT24,

title = {RobTL: Robustness Temporal Logic for CPS},

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

editor = {Rupak Majumdar and Alexandra Silva},

url = {https://doi.org/10.4230/LIPIcs.CONCUR.2024.15},

doi = {10.4230/LIPICS.CONCUR.2024.15},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {35th International Conference on Concurrency Theory, CONCUR 2024, 

 September 9-13, 2024, Calgary, Canada},

volume = {311},

pages = {15:1–15:23},

publisher = {Schloss Dagstuhl - Leibniz-Zentrum für Informatik},

series = {LIPIcs},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/fmics/CastiglioniLLT24,

title = {Evaluating the Effectiveness of Digital Twins Through Statistical  Model Checking with Feedback and Perturbations},

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

editor = {Anne E. Haxthausen and Wendelin Serwe},

url = {https://doi.org/10.1007/978-3-031-68150-9_2},

doi = {10.1007/978-3-031-68150-9_2},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {Formal Methods for Industrial Critical Systems - 29th International 

 Conference, FMICS 2024, Milan, Italy, September 9-11, 2024, Proceedings},

volume = {14952},

pages = {21–39},

publisher = {Springer},

series = {Lecture Notes in Computer Science},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{bowles2024enhancing,

title = {Enhancing Reaction Systems with Guards for Analysing Comorbidity Treatment Strategies},

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

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {International Conference on Computational Methods in Systems Biology (CMSB 2024)},

volume = {LNCS 14971},

pages = {27–44},

organization = {Springer},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/cmsb/BallisBFO24,

title = {Process Calculi and Rewriting Techniques for Analyzing Reaction Systems},

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

editor = {Roberta Gori and Paolo Milazzo and Mirco Tribastone},

url = {https://doi.org/10.1007/978-3-031-71671-3_1},

doi = {10.1007/978-3-031-71671-3_1},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {Computational Methods in Systems Biology - 22nd International Conference, 

 CMSB 2024, Pisa, Italy, September 16-18, 2024, Proceedings},

volume = {14971},

pages = {1–18},

publisher = {Springer},

series = {Lecture Notes in Computer Science},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/icfem/Sproston24,

title = {Clock-Dependent Probabilistic Timed Automata with One Clock and No  Memory},

author = {Jeremy Sproston},

editor = {Kazuhiro Ogata and Dominique Méry and Meng Sun and Shaoying Liu},

doi = {10.1007/978-981-96-0617-7_5},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {Proceedings of the 25th International Conference 

 on Formal Engineering Methods (ICFEM 2024)},

volume = {15394},

pages = {70–84},

publisher = {Springer},

series = {Lecture Notes in Computer Science},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{graziani2024the,

title = {The Expressive Power of Path-Based Graph Neural Networks},

author = {Caterina Graziani and Tamara Drucks and Fabian Jogl and Monica Bianchini and scarselli and Thomas Gärtner},

url = {https://openreview.net/forum?id=io1XSRtcO8},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {Forty-first International Conference on Machine Learning},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{brodo2024attractor,

title = {Attractor and Slicing Analysis of a T Cell Differentiation Model Based on Reaction Systems},

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

year  = {2023},

date = {2023-11-07},

booktitle = {International Symposium ``from Data to Models and Back'' (DataMod 2023)},

volume = {LNCS},

organization = {Springer},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{pham2025preliminaryb,

title = {Preliminary Results on Shapley Value Notions and Propagation Methods for Boolean Networks},

author = {Giang Pham and Paolo Milazzo},

year  = {2023},

date = {2023-11-07},

booktitle = {International Symposium ``from Data to Models and Back'' (DataMod 2023)},

volume = {LNCS},

organization = {Springer},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{<LineBreak>graziani2023no,

title = {No PAIN no Gain: More Expressive GNNs with Paths},

author = {Caterina Graziani and Tamara Drucks and Monica Bianchini and scarselli and Thomas Gärtner},

url = {https://openreview.net/forum?id=q2xXh4M9Dx},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {NeurIPS 2023 Workshop: New Frontiers in Graph Learning},

abstract = {Motivated by the lack of theoretical investigation into the discriminative power of paths, we characterize classes of graphs where paths are sufficient to identify every instance. Our analysis motivates the integration of paths into the learning procedure of graph neural networks in order to enhance their expressiveness. We formally justify the use of paths based on finite-variable counting logic and prove the effectiveness of paths to recognize graph structural features related to cycles and connectivity. We show that paths are able to identify graphs for which higher-order models fail. Building on this, we propose PAth Isomorphism Network (PAIN), a novel graph neural network that replaces the topological neighborhood with paths in the aggregation step of the message-passing procedure. This modification leads to an algorithm that is strictly more expressive than the Weisfeiler-Leman graph isomorphism test, at the cost of a polynomial-time step for every iteration and fixed path length. We support our theoretical findings by empirically evaluating PAIN on synthetic datasets.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}