Thesis etd-02192020-165904 |
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Thesis type
Tesi di laurea magistrale
Author
BACHINI, FRANCESCO
URN
etd-02192020-165904
Thesis title
On the use of sequential learning models to estimate natural selection on HIV from clinical samples
Department
INFORMATICA
Course of study
DATA SCIENCE AND BUSINESS INFORMATICS
Supervisors
relatore Bacciu, Davide
Keywords
- gru
- hiv
- machine learning
- selection coefficient
Graduation session start date
06/03/2020
Availability
None
Summary
The thesis deals with the analysis of artificial and real-world clinical data concerning natural selection on the HIV virus. A sampling algorithm has been implemented to generate synthetic data for preliminary validation and training set expansion. A recurrent neural architecture has been deployed, trained and tested on the task of estimating the selection coefficient in the HIV virus.
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