Sofia Borgato
Graph Neural Network for the prediction of Antibiotic Resistance.
Rel. Giovanni Squillero, Alberto Paolo Tonda, Pietro Barbiero, Giulio Ferrero. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
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Abstract
Antimicrobial resistance is one of the main threats to global health. Antibiotics still represent the most reliable solution for bacterial infection treatment, but the spread of antibiotic resistance threatens their efficacy resulting in a dramatic worldwide increase in morbidity and mortality. Each bacterial species is often only susceptible to a few specific antibiotics; this is why it is critical to identify the correct and still effective antibiotic to be used in a clinical setting. Although genetic testing of bacteria is increasingly used in the medical lab, a time-consuming antibiogram, based on bacterial cultures, is still the standard approach. Current techniques make it possible to sequence bacterial DNA in a much shorter time, allowing the discrimination of a resistant bacterium from a susceptible one based on the presence of specific elements that confer resistance.
This thesis aims to perform this task on general basis, classifying bacteria genomes through Graph Neural Networks (GNN)
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