# SPN-PBP-AMR ## PBP Analysis and Predicted MICs This *Streptococcus pneumoniae* task carries out an analysis of the *pbp1A*, *pbp2B* and *pbp2X* genes, firstly assigning an allele code to each one and then using a machine learning approach to estimate the MICs (minimum inhibitory concentration) for a set of antimicrobials. The resistance phenotype is then interpreted using CLSI guidelines, including where there are different thresholds for the meningital and non-meningital forms. In testing, the MICs were predicted to >97% correct (to one dilution) and resistance categorisation was >93%. The initial development of the machine learning model is described in [Li *et al* (2016)](https://www.ncbi.nlm.nih.gov/pubmed/27302760) while the method is benchmarked in [Li *et al* (2017)](https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-017-4017-7). The software and databases for this task have been provided by Ben Metcalf at the Centre for Disease Control (CDC) and is available from