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

  12 June 2019

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. 

Further reading: MacroEconomicsLab
Effective Surveillance   Smart Innovations  
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