# 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 --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://cgps.gitbook.io/pathogenwatch/technical-descriptions-of-analysis-tools/antimicrobial-resistance-prediction/spn-pbp-amr.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.