Antibiotic Resistance Determination Utilizing Machine Learning

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Summary Our long-term objective is to tackle antibiotic resistance by developing accurate and interpretable prediction machine learning models that could be used clinically to speed up the care of patients with bacterial infections. Current approaches to diagnosing bacterial infections rely on first culturing a pathogen from a collected specimen followed by a variety of phenotypic tests to determine what antibiotic a particular bacterial isolate would be sensitive or resistant to. This process can, in many cases, take days to finish. Developing an accurate way to predict antibiotic resistance utilizing whole-genome sequencing data without the need for phenotypic testing is the overall goal of this project. Our team applies the latest advances in deep learning and cloud computation. We will pursue the following Specific Aims: 1) Curate a large dataset and develop a deep-learning prediction model with state-of-the-art accuracies for a wide range of bacterial species and antibiotic combinations; 2) Develop personalized machine learning models for chronic infections; 3) create open-sourced scalable user- friendly resources for the broad research community. The successful completion of this work will provide a paradigm shift in the way we diagnose bacterial infections and speed up the time to providing the correct antibiotic for a specific pathogen.