Evaluation of machine learning and rules-based approaches for predicting antimicrobial resistance profiles in gram-negative bacilli from whole genome sequence data

Frontiers in Microbiology

Volumn 7, Issue NOV, 2016, Pages

  Pesesky, Mitchell W ^a,d   Hussain, Tahir ^a,b,e   Wallace, Meghan ^a   Patel, Sanket ^a   Andleeb, Saadia ^b   Burnham, Carey Ann D ^a   Dantas, Gautam ^a,c  

^a WASHINGTON UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b NATIONAL UNIVERSITY OF SCIENCES AND TECHNOLOGY   (Pakistan)

^c WASHINGTON UNIVERSITY IN ST LOUIS   (United States)

^d University of Washington   (United States)

^e ABDUL WALI KHAN UNIVERSITY   (Pakistan)

Author keywords

Aminoglycoside; Antibiotics; Beta lactam; Diagnostics; Enterobacteriaceae; Machine learning; Resistance; Tetracycline

Indexed keywords

AMPICILLIN; BETA LACTAM ANTIBIOTIC; BETA LACTAMASE; CEFAZOLIN; CEFEPIME; CEFOTETAN; CEFTAZIDIME; CEFTRIAXONE; CHLORAMPHENICOL; CIPROFLOXACIN; COTRIMOXAZOLE; DOXYCYCLINE; GENTAMICIN; MEROPENEM;

ACCURACY; ALGORITHM; AMINO ACID SEQUENCE; ANTIBIOTIC RESISTANCE; ANTIBIOTIC SENSITIVITY; ARTICLE; BACTERIAL GENE; BACTERIAL GENOME; CONTROLLED STUDY; DATA BASE; ENTEROBACTERIACEAE; GENE IDENTIFICATION; GENE MUTATION; GENOTYPE; GRAM NEGATIVE BACTERIUM; MACHINE LEARNING; METAGENOMICS; MULTIDRUG RESISTANCE; OPEN READING FRAME; PHENOTYPE; PREDICTION; RECEIVER OPERATING CHARACTERISTIC; RESISTANCE GENE; RULES BASED ALGORITHM;

EID: 85006705701     PISSN: None     EISSN: 1664302X     Source Type: Journal    
DOI: 10.3389/fmicb.2016.01887     Document Type: Article

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