Tesi etd-06262024-124845 |
Link copiato negli appunti
Tipo di tesi
Tesi di laurea magistrale
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 (Inglese)
Riassunto (Italiano)
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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