Machine learning models for
 Neisseria gonorrhoeae
 antimicrobial susceptibility tests

Neisseria gonorrhoeae is an urgent public health threat due to the emergence of antibiotic 24 resistance. As most isolates in the US are susceptible to at least one antibiotic, rapid molecular 25 antimicrobial susceptibility tests (ASTs) would offer the opportunity to tailor antibiotic therapy, 26 thereby expanding treatment options. With genome sequence and antibiotic resistance 27 phenotype data for nearly 20,000 clinical N. gonorrhoeae isolates now available, there is an 28 opportunity to use statistical methods to develop sequence-based diagnostics that predict 29 antibiotic susceptibility from genotype. N. gonorrhoeae therefore provides a useful example 30 illustrating how to apply machine learning models to aid in the design of sequence-based ASTs. 31 We present an overview of this framework, which begins with establishing the assay technology, 32 the performance criteria, the population in which the diagnostic will be used, and the clinical 33 goals, and extends to the choices that must be made to arrive at a set of features with the 34 desired properties for predicting susceptibility phenotype from genotype. While we focus on the 35 example of N. gonorrhoeae , the framework generalizes to other organisms for which large-scale 36 genotype and antibiotic resistance data can be combined to aid in diagnostics development. 37