Thesis etd-09202023-221533 |
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Thesis type
Tesi di laurea magistrale
URN
etd-09202023-221533
Thesis title
Predicting Dynamical Properties of Pathways from Protein Interaction Networks using Graph Neural Networks
Department
INFORMATICA
Course of study
INFORMATICA
Supervisors
.
relatore Prof. Micheli, Alessio
relatore Prof. Milazzo, Paolo
relatore Dott. Podda, Marco
relatore Prof. Milazzo, Paolo
relatore Dott. Podda, Marco
Keywords
- artificial intelligence
- dynamical systems
- graph neural networks
- machine learning
- proteins
- supervised learning
- systems biology
Graduation session start date
06/10/2023
Availability
Full
Abstract (Inglese)
Abstract (Italiano)
This thesis focuses on the application of machine learning techniques to explore the relationship between biochemical pathways and protein-protein interaction (PPi) networks in biological systems. PPi networks, with limited information compared to pathways, present challenges in inferring relevant properties on the emerging behavior of a complex system. The work demonstrates that the dynamical information coming from the pathway level can still be predicted on PPi networks using graph neural networks.
File
| Nome file | Dimensione |
|---|---|
| Tesi_GNN...Pi_34.pdf | 3.08 Mb |
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