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Convolutional Networks for predicting Antimicrobial Resistance

Simone Alessandri'

Convolutional Networks for predicting Antimicrobial Resistance.

Rel. Giovanni Squillero, Alberto Paolo Tonda, Pietro Barbiero, Giulio Ferrero. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Biomedica, 2021

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Abstract:

Antibiotic resistance is a growing public health concern worldwide. Many diagnostic techniques were developed, but none of them are fast enough to predict the presence of bacteria with a resistant phenotype. This work proposes the use of a convolutional neural network on bacterial DNA sequences to predict resistance. Once the optimal neural network architecture was selected, validation was performed using a k-fold procedure estimating the loss function of both validation and test and evaluating the confusion matrix. Thanks to this technology it would be possible to speed up the choice of a proper therapeutic strategy and to avoid the rise of untreatable infection diseases.

Relatori: Giovanni Squillero, Alberto Paolo Tonda, Pietro Barbiero, Giulio Ferrero
Anno accademico: 2021/22
Tipo di pubblicazione: Elettronica
Numero di pagine: 111
Soggetti:
Corso di laurea: Corso di laurea magistrale in Ingegneria Biomedica
Classe di laurea: Nuovo ordinamento > Laurea magistrale > LM-21 - INGEGNERIA BIOMEDICA
Aziende collaboratrici: NON SPECIFICATO
URI: http://webthesis.biblio.polito.it/id/eprint/21676
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