A machine learning approach to C9ORF72 and sex-based 2-[18F]FDG-PET stratification differences in ALS heterogeneity
Bauer, David (A.A. 2024/2025) A machine learning approach to C9ORF72 and sex-based 2-[18F]FDG-PET stratification differences in ALS heterogeneity. Tesi di Laurea in Data science in action, Luiss Guido Carli, relatore Alessio Martino, pp. 62. [Master's Degree Thesis]
Full text for this thesis not available from the repository.
Abstract/Index
Literature review. Machine learning and neuroimaging in ALS. Prediction and stratification in [18F]FDG PET studies. Methods. Data set extraction and preprocessing. Differential network analysis. Prediction optimization and feature selection. Results. Data set statistics. Differential network analysis. Predictive feature exploration. Prediction validation. Unpromising previous experiments.
References
Bibliografia: pp. 30-33.
| 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: | Sinaimeri, Blerina |
| Academic Year: | 2024/2025 |
| Session: | Summer |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 08 Jan 2026 13:13 |
| Last Modified: | 08 Jan 2026 13:13 |
| URI: | https://tesi.luiss.it/id/eprint/44623 |
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