Tesi etd-06262024-124845 |
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Tipo di tesi
Tesi di laurea magistrale
Autore
BOENING, DAVID
URN
etd-06262024-124845
Titolo
eBWT String Kernels in the Context of Bioinformatics
Dipartimento
INFORMATICA
Corso di studi
INFORMATICA
Relatori
relatore Prof. Micheli, Alessio
relatore Prof.ssa Rosone, Giovanna
relatore Prof.ssa Rosone, Giovanna
Parole chiave
- bioinformatics
- Burrows-Wheeler transform
- BWT
- eBWT
- kernel
- machine learning
- ML
- support vector machines
- SVM
Data inizio appello
12/07/2024
Consultabilità
Non consultabile
Data di rilascio
12/07/2027
Riassunto
This study aims to develop and evaluate novel string kernels based on the extended
Burrows-Wheeler transform (eBWT). We evaluated the performance of these methods
using support vector machines (SVM) machine learning models trained on multiple
biological datasets compared against other well-established kernels. Results show that
eBWT kernels perform especially well on long sequences.
Burrows-Wheeler transform (eBWT). We evaluated the performance of these methods
using support vector machines (SVM) machine learning models trained on multiple
biological datasets compared against other well-established kernels. Results show that
eBWT kernels perform especially well on long sequences.
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