A machine learning approach for physiological role prediction in protein contact networks: a large-scale analysis on the human proteome
Cervellini, Mattia (A.A. 2024/2025) A machine learning approach for physiological role prediction in protein contact networks: a large-scale analysis on the human proteome. Tesi di Laurea in Data science in action, Luiss Guido Carli, relatore Alessio Martino, pp. 91. [Master's Degree Thesis]
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Abstract/Index
Biological motivation. Enzymatic proteins and the EC numbering system. Graph-based representations of protein structures. Research objectives and tasks. Literature review. Theoretical background. Previous work on proteins’ physiological role prediction. Data collection. Methodologies. Proteins as PCN-graphs. Graph embedding via simplicial complexes. (hyper)graph kernels. Spectral density. Summary of representation techniques. Standard classifiers. Summary of standard classification algorithms. GNNs. Performance metrics. Hyperparameter optimization strategy. Data resampling and splitting strategy. Results.
References
Bibliografia: pp. 69-80.
| Thesis Type: | Master's Degree Thesis |
|---|---|
| Institution: | Luiss Guido Carli |
| Degree Program: | Master's Degree Programs > Master's Degree Program in Data Science e Management (LM-91) |
| Chair: | Data science in action |
| Thesis Supervisor: | Martino, Alessio |
| Thesis Co-Supervisor: | Sozio, Mauro |
| Academic Year: | 2024/2025 |
| Session: | Autumn |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 10 Feb 2026 14:06 |
| Last Modified: | 10 Feb 2026 14:06 |
| URI: | https://tesi.luiss.it/id/eprint/44757 |
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