Eugenio Dosualdo
Explainable deep-learning techniques for the study of antibiotic resistance in bacterial infectants.
Rel. Giovanni Squillero, Alberto Paolo Tonda, Pietro Barbiero. Politecnico di Torino, Master of science program in Computer Engineering, 2022
Abstract
AMR has been listed as one of the leading public health threats of the 21st century, with some estimates predicting it to cause the deaths of 10 million people a year by 2050. WHO and numerous other groups and researchers agree that the spread of AMR is an urgent issue requiring a global, coordinated action plan to address, and more and more publications are addressing the issue and estimating the ARM burden in terms of health and public health expenses. State-of-the-art sequencing techniques (next generation sequencing, NGS) have made it possible in recent years to obtain the genome sequence of an organism in short time and at low cost, thus enabling a study of resistance through DNA analysis.
In particular, promising results have been obtained through Machine Learning techniques applied to the gene sequence to identify the parts responsible for drug resistance
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