Our knowledge of resistance of bacteria, such as E. coli, to antibiotic drugs is incomplete. An understanding of how such resistance is passed on from generation to generation of bacteria is important in helping us to determine which drugs should be given to patients, and in helping us formulate new drugs. This project aims to be use “big data” methods to combine information from many infection-related databases, with the goal of improving our understanding of how resistance to different types of drug are inherited by bacteria such as E. coli. The “gold standard” means of testing resistance to drugs is a series of laboratory tests, however such tests vary from hospital to hospital (and, over time, even within the same hospital), and where resistance to different sets of drugs are typically tested. Also, the results of these tests are imperfect, and occasional mislabelling occurs (where a test might, for example, indicate that a bacteria sample is resistant to a drug, when in fact it is not resistant). With data being collected across many hospitals, there is great potential for combining these datasets using modern methods of machine learning. Such methods are able to cope with occasionally-mislabelled data, and can be used to combine datasets of different kinds: for example, this project will consider the results of lab tests from blood and urine samples, data concerning the severity of infection of the patient from which each sample came, and, for some samples, their genomic sequences.