| KEYWORD |
Deep Learning for DNA Analysis
keywords ANTIBIOTICS, DRUG RESISTANCE, BACTERIA, DRUG DESIG, DEEP NEURAL NETWORKS, MACHINE LEARNING
Reference persons GIOVANNI SQUILLERO
External reference persons Giulio Ferrero (UniTO); Alberto Tonda (INRAE, France); Pietro Barbiero (University of Cambridge, UK).
Research Groups DAUIN - GR-05 - ELECTRONIC CAD & RELIABILITY GROUP - CAD
Thesis type RESEARCH / EXPERIMENTAL
Description Deep Neural Networks are able to detect and exploit relationships in sequences of data, and deep Neural Networks could be precious to tackle important medical issues. For instance, convolutional Neural Networkss have been recently applied to the bioinformatics domain for studying strands of RNA from SARS-CoV-2 (the virus responsible for COVID-19), and the research led to the definition of a reliable test.
The research group involved in the research is composed of cross-domain experts from 4 different European research institutions and a Start-up company. We already performed a feasibility study using standard Machine Learning and Neural Networks to predict sensitivity or resistance to antibiotics for bacteria, analyzing their DNA as a sequence of data. The candidate should help bringing the research one step forward: exploit bleeding-edge Neural Networks to identify cross-species markers able to predict sensitivity or resistance to specific antibiotics.
Deadline 01/01/2022
PROPONI LA TUA CANDIDATURA