Antibiotic Resistance in low- and middle-income countries and the prospect of Machine Learning approaches to fight against this threat

Big data informatics has enabled scientists to predict antimicrobial resistance and associated genomic features from Whole-Genome Sequencing. The genomics revolution has led to thousands of strain-specific whole-genome sequences available for a range of pathogenic bacteria. These genomes are increasingly coupled with clinical antimicrobial resistance (AMR) metadata, including MIC values for various antibiotics. This large-scale coupling of AMR data with strain-specific genome sequences opens the study of antibiotic resistance to machine learning and other big data science approaches.