A k-mer-based method for the identification of phenotype-associated genomic biomarkers and predicting phenotypes of sequenced bacteria

PLoS Computational Biology

Volumn 14, Issue 10, 2018, Pages

  Aun, Erki ^a   Brauer, Age ^a   Kisand, Veljo ^b   Tenson, Tanel ^b   Remm, Maido ^a  

^a UNIVERSITY OF TARTU   (Estonia)

^b UNIVERSITY OF TARTU   (Estonia)

Author keywords

[No Author keywords available]

Indexed keywords

BACTERIA; COMPUTER OPERATING SYSTEMS; FORECASTING; GENES; GENOME; LARGE DATASET; OPEN SYSTEMS;

AZITHROMYCIN; BACTERIAL ISOLATES; CLOSTRIDIUM DIFFICILE; GENOMICS; KLEBSIELLA PNEUMONIAE; MEMORY EFFICIENT; PREDICTION MODELLING; PSEUDOMONAS AERUGINOSA; STATISTIC MODELING; TEST SETS;

OPEN SOURCE SOFTWARE;

AZITHROMYCIN; CIPROFLOXACIN; BACTERIAL DNA;

ANTIBIOTIC RESISTANCE; ARTICLE; BACTERIAL GENOME; BACTERIUM ISOLATE; CONTROLLED STUDY; DATA ANALYSIS; GENE IDENTIFICATION; GENE SEQUENCE; GENETIC ANALYSIS; GENETIC MARKER; KLEBSIELLA PNEUMONIAE; MACHINE LEARNING; NONHUMAN; PEPTOCLOSTRIDIUM DIFFICILE; PHENOTYPE; PREDICTION; PSEUDOMONAS AERUGINOSA; SEQUENCE ANALYSIS; STATISTICAL MODEL; VALIDATION STUDY; ALGORITHM; BACTERIUM; DNA SEQUENCE; GENETICS; GENOMICS; METABOLISM; PHYSIOLOGY; PROCEDURES; SEQUENCE ALIGNMENT; SOFTWARE;

ALGORITHMS; BACTERIA; DNA, BACTERIAL; GENETIC MARKERS; GENOME, BACTERIAL; GENOMICS; PHENOTYPE; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS, DNA; SOFTWARE;

EID: 85055913635     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1006434     Document Type: Article

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