Machine learning techniques accurately classify microbial communities by bacterial vaginosis characteristics

PLoS ONE

Volumn 9, Issue 2, 2014, Pages

  Beck, Daniel ^a   Foster, James A ^a  

^a UNIVERSITY OF IDAHO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ARTICLE; CLASSIFICATION; CLASSIFIER; ENVIRONMENTAL FACTOR; FEMALE; HUMAN; LACTOBACILLUS; LOGISTIC REGRESSION ANALYSIS; MACHINE LEARNING; MICROBIAL COMMUNITY; MICROBIOME; NONHUMAN; POPULATION SIZE; SCORING SYSTEM; VAGINITIS;

ARTIFICIAL INTELLIGENCE; BACTERIA; FEMALE; HUMANS; PHYLOGENY; POLYMERASE CHAIN REACTION; RNA, RIBOSOMAL, 16S; VAGINA; VAGINOSIS, BACTERIAL;

EID: 84896842823     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0087830     Document Type: Article

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