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대학원 세미나


03/07(목)16시연사:김지웅
작성자 관리자조회수 321날짜 2019.02.27
일시: 03월 07일(목) 4:00PM

장소 : 벤처관 711호

연사: 김지웅

제목: VAMPr: variant mapping and prediction of antibiotic resistance via explainable features and machine learning

내용:Antibiotic resistance is an increasing threat to public health. Current methods of determining resistance rely on inefficient phenotypic approaches. There is interest in developing rapid algorithms that could utilize genomic information in order to predict phenotype. To precisely understand the connection between sequence variations and antibiotic resistance, we sought data-driven approaches to offer explainable insights for the link between genetics and antibiotic resistance. We developed a new bioinformatics tool, variant mapping and prediction of antibiotic resistance (VAMPr), to (1) derive gene ortholog-based sequence features for variants; (2) interrogate these explainable gene-level variants for their known or novel associations with antibiotic resistance; and (3) build accurate models to predict antibiotic resistance based on whole genome sequencing data. Following the Clinical & Laboratory Standards Institute (CLSI) guidelines, we curated the sequence data of 3,393 bacterial isolates from 9 species, and their antibiotic resistance phenotypes for 29 antibiotics. Together we detected 14,615 variant genotypes and built 93 association and prediction models. The association models confirmed known genetic antibiotic resistance mechanisms. The prediction models achieved high accuracies (mean accuracy of 91.1% for all antibiotic-pathogen combinations) internally through nested cross validation and were also validated using external clinical datasets. The VAMPr variant detection method, association and prediction models will be valuable tools for antibiotic resistance research for basic scientists and clinicians.
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다음 12/13(목)16시연사:박준형대표((주)쓰리빅스)