Towards large-scale FAME-based bacterial species identification using machine learning techniques

Systematic and Applied Microbiology

Volumn 32, Issue 3, 2009, Pages 163-176

  Slabbinck, Bram ^a   De Baets, Bernard ^a   Dawyndt, Peter ^a   De Vos, Paul ^b  

^a GHENT UNIVERSITY   (Belgium)

^b GHENT UNIVERSITY   (Belgium)

Author keywords

Bacillus; Bacteria; Fatty acid methyl ester; Gas chromatography; Identification; Machine learning; Paenibacillus; Pseudomonas; Species; Taxonomy

Indexed keywords

FATTY ACID ESTER;

ARTICLE; ARTIFICIAL NEURAL NETWORK; BACILLUS; BACTERIAL GROWTH; BACTERIUM IDENTIFICATION; DATA BASE; GENUS; INTERMETHOD COMPARISON; MACHINE LEARNING; MATHEMATICAL MODEL; NONHUMAN; PAENIBACILLUS; PRIORITY JOURNAL; PSEUDOMONAS; RANDOM FOREST; SPECIES IDENTIFICATION; SUPPORT VECTOR MACHINE; TAXONOMY;

ARTIFICIAL INTELLIGENCE; BACILLUS; BACTERIA; BACTERIAL TYPING TECHNIQUES; CHROMATOGRAPHY, GAS; FATTY ACIDS; NEURAL NETWORKS (COMPUTER); PSEUDOMONAS; SPECIES SPECIFICITY;

BACILLUS (BACTERIUM); BACTERIA (MICROORGANISMS); PAENIBACILLUS; PROKARYOTA; PSEUDOMONAS;

EID: 62649092955     PISSN: 07232020     EISSN: 16180984     Source Type: Journal    
DOI: 10.1016/j.syapm.2009.01.003     Document Type: Article

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