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Machine learning of antimicrobial resistance
Derya Aytan-Aktug
Research Group for Genomic Epidemiology
National Food Institute
Research output
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Book/Report
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Ph.D. thesis
234
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Dive into the research topics of 'Machine learning of antimicrobial resistance'. Together they form a unique fingerprint.
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Biochemistry, Genetics and Molecular Biology
Antibiotic Resistance
100%
Learning
100%
Plasmid
52%
Phenotype
36%
Accuracy
21%
Random Forest
15%
Computer Model
10%
Chromosome
10%
Host Range
10%
Species
10%
Comprehension
10%
Solution and Solubility
10%
Infectious Agent
10%
Nested Gene
5%
Bacteriophage
5%
Organisms
5%
Plasticity
5%
Artificial Neural Network
5%
Horizontal Gene Transfer
5%
Bacterial Genome
5%
Threat
5%
Host
5%
Animal Health
5%
Life
5%
Taxonomy
5%
Genetic Resistance
5%
Genotype
5%
Immunology and Microbiology
Antimicrobial Resistance
100%
Learning
100%
Phenotype
36%
Accuracy
21%
Metagenomics
21%
Random Forest
15%
Computer Model
10%
Chromosome
10%
Host Range
10%
Classifier
10%
Species
10%
Comprehension
10%
Infectious Agent
10%
Solution and Solubility
10%
Organisms
5%
Genotype
5%
Artificial Neural Network
5%
Host
5%
Bacterial Genome
5%
Animal Health
5%
Plasticity
5%
Taxonomy
5%
Horizontal Gene Transfer
5%
Genetic Resistance
5%
Gene
5%
Life
5%