Graph neural networks with application in bioinformatics
Probst, Claire Sophie (A.A. 2024/2025) Graph neural networks with application in bioinformatics. Tesi di Laurea in Data visualization, Luiss Guido Carli, relatore Blerina Sinaimeri, pp. 65. [Master's Degree Thesis]
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Abstract/Index
Methodology. Data collection and dataset definitions. Overview of data sources and computational tools. Node entity and relationship data collection. Node attribute collection. Node attributes embedding and feature representation. Heterogeneous network construction. Node and edge construction. Conversion to undirected graph. PyG HeteroData representation. Relational graph neural network construction and training. R-GCN architecture. Training procedure. Results and discussion. Evaluation of metrics. Model training and convergence. Link prediction performance evaluation. Bi-adjacency matrix reconstruction. Analysis of known vs unknown associations. Identification of novel drug-side effect associations.
References
Bibliografia: pp. 60-64.
| 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 visualization |
| Thesis Supervisor: | Sinaimeri, Blerina |
| Thesis Co-Supervisor: | Martino, Alessio |
| Academic Year: | 2024/2025 |
| Session: | Extraordinary |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 14 Jul 2026 13:53 |
| Last Modified: | 14 Jul 2026 13:53 |
| URI: | https://tesi.luiss.it/id/eprint/46395 |
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