Machine Learning for Rapid Diagnosis of Antimicrobial Resistance in I Streptococcus pneumoniae /I ---Chinese Academy of Sciences

Streptococcus pneumoniae is the most common human respiratory pathogen, and β-lactam antibiotics have been employed to treat infections caused by S. pneumoniae for decades. However, the high variability of PBPs in clinical isolates and their mosaic gene structure hamper the predication of resistance level according to the PBP gene sequences. A research group led by Prof. FENG Jie at Institute of Microbiology of the Chinese Academy of Sciences developed a systematic strategy for applying supervised machine learning (SL) to predict antimicrobial susceptibility testing (AST) of β-lactam antibiotic resistance. The study was published in Briefings in Bioinformatics. The published PBP sequences with minimum inhibitory concentration (MIC) values and the sequences from NCBI database without MIC values were served as labelled data and unlabeled data, respectively. The performances of SL models were evaluated by cross-validation: the labelled data set was randomly split into 80% training set and 20% test set 100 times.