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  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