VAMPr: VAriant Mapping and Prediction of antibiotic resistance via explainable features and machine learning

Antimicrobial resistance (AMR) is an increasing threat to public health. Current methods of determining AMR rely on inefficient phenotypic approaches, and there remains incomplete understanding of AMR mechanisms for many pathogen-antimicrobial combinations. Given the rapid, ongoing increase in avail...

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Veröffentlicht in:	PLoS computational biology 2020-01, Vol.16 (1), p.e1007511-e1007511
Hauptverfasser:	Kim, Jiwoong, Greenberg, David E, Pifer, Reed, Jiang, Shuang, Xiao, Guanghua, Shelburne, Samuel A, Koh, Andrew, Xie, Yang, Zhan, Xiaowei
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Amino acid sequence Anti-Bacterial Agents - pharmacology Antibiotic resistance Antibiotics Antimicrobial agents Antimicrobial resistance Archives & records Authorship Bacteria - drug effects Bacteria - genetics Bioinformatics Biology and Life Sciences Biomedical research Cancer Chromosome Mapping Datasets Deoxyribonucleic acid DNA DNA, Bacterial - analysis DNA, Bacterial - genetics Drug resistance Drug Resistance, Microbial - genetics Funding Gene mapping Gene sequencing Genes Genome, Bacterial - genetics Genomes Genotype & phenotype Genotypes Health risks Information processing Internal medicine Learning algorithms Machine Learning Mapping Medicine and Health Sciences Methods Model accuracy Models, Statistical Pathogens Phenotypes Phenotypic variations Physical Sciences Population Prediction models Public health Research and Analysis Methods Software Streptococcus infections Supervision Whole genome sequencing Whole Genome Sequencing - methods
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