Tesi etd-02062026-190804 |
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Tipo di tesi
Tesi di laurea magistrale
Autore
BOLDRINI, FRANCESCO
URN
etd-02062026-190804
Titolo
Using Geometric Deep Learning and Generative Artificial Intelligence for Aptamer Predictive Triage
Dipartimento
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
ARTIFICIAL INTELLIGENCE AND DATA ENGINEERING
Relatori
relatore Prof. Cimino, Mario Giovanni Cosimo Antonio
relatore Prof. Galatolo, Federico Andrea
relatore Prof. Galatolo, Federico Andrea
Parole chiave
- ai
- aptamer
- aptamers
- artificial intelligence
- bayesian optimization
- data analysis
- deep learning
- machine learning
- protein
- proteins
Data inizio appello
27/02/2026
Consultabilità
Non consultabile
Data di rilascio
27/02/2096
Riassunto (Inglese)
Riassunto (Italiano)
This thesis proposes the use of Geometric Deep Learning and Generative Artificial Intelligence for Aptamer Predictive Triage, in order to explore methods to hasten or entirely overcome the traditional SELEX approach.
Recent advancements in Geometric Deep Learning and generative artificial intelligence allow the study of alternative methods for in-silico de-novo aptamer research.
In particular, this approach looks into the possibility of enhancing current methodologies with spatial information about both proteins, aptamers and their binding sites.
Recent advancements in Geometric Deep Learning and generative artificial intelligence allow the study of alternative methods for in-silico de-novo aptamer research.
In particular, this approach looks into the possibility of enhancing current methodologies with spatial information about both proteins, aptamers and their binding sites.
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