Machine learning: novel bioinformatics approaches for combating antimicrobial resistanceMacesic, Nenada,b,c; Polubriaginof, Fernandab; Tatonetti, Nicholas P.bCurrent Opinion in Infectious Diseases: December 2017 - Volume 30 - Issue 6 - p 511–517 doi: 10.1097/QCO.0000000000000406 ANTIMICROBIAL AGENTS: BACTERIAL/FUNGAL: Edited by Monica Slavin Abstract Author InformationAuthors Article MetricsMetrics Purpose of review Antimicrobial resistance (AMR) is a threat to global health and new approaches to combating AMR are needed. Use of machine learning in addressing AMR is in its infancy but has made promising steps. We reviewed the current literature on the use of machine learning for studying bacterial AMR. Recent findings The advent of large-scale data sets provided by next-generation sequencing and electronic health records make applying machine learning to the study and treatment of AMR possible. To date, it has been used for antimicrobial susceptibility genotype/phenotype prediction, development of AMR clinical decision rules, novel antimicrobial agent discovery and antimicrobial therapy optimization. Summary Application of machine learning to studying AMR is feasible but remains limited. Implementation of machine learning in clinical settings faces barriers to uptake with concerns regarding model interpretability and data quality. Future applications of machine learning to AMR are likely to be laboratory-based, such as antimicrobial susceptibility phenotype prediction. aDivision of Infectious Diseases, Columbia University Medical Center bDepartment of Biomedical Informatics, Columbia University, New York City, New York, USA cDepartment of Infectious Diseases, Austin Health, Heidelberg, Victoria, Australia Correspondence to Nenad Macesic, 630W 168th Street, New York, NY 10032, USA. Tel: +1 212 305 7185; e-mail: firstname.lastname@example.org Copyright © 2017 Wolters Kluwer Health, Inc. All rights reserved.