Machine Learning Techniques Accurately Classify Microbial Communities by Bacterial Vaginosis Characteristics

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Daniel Beck and James A. Foster",
  title =        "Machine Learning Techniques Accurately Classify
                 Microbial Communities by Bacterial Vaginosis
  journal =      "PLoS ONE",
  year =         "2014",
  volume =       "9",
  number =       "2",
  pages =        "e87830",
  month =        feb # " 3",
  keywords =     "genetic algorithms, genetic programming, Bacterial
                 vaginosis, Microbiome, Lactobacillus, Vagina, Community
                 ecology, Machine learning algorithms",
  keywords =     "genetic algorithms, genetic programming",
  bibsource =    "OAI-PMH server at",
  publisher =    "Public Library of Science",
  oai =          "",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1371/journal.pone.0087830",
  size =         "8 pages",
  abstract =     "Microbial communities are important to human health.
                 Bacterial vaginosis (BV) is a disease associated with
                 the vagina microbiome. While the causes of BV are
                 unknown, the microbial community in the vagina appears
                 to play a role. We use three different machine-learning
                 techniques to classify microbial communities into BV
                 categories. These three techniques include genetic
                 programming (GP), random forests (RF), and logistic
                 regression (LR). We evaluate the classification
                 accuracy of each of these techniques on two different
                 datasets. We then deconstruct the classification models
                 to identify important features of the microbial
                 community. We found that the classification models
                 produced by the machine learning techniques obtained
                 accuracies above 90percent for Nugent score BV and
                 above 80percent for Amsel criteria BV. While the
                 classification models identify largely different sets
                 of important features, the shared features often agree
                 with past research.",
  notes =        "16 S rRNA, Random Forests, Logistic Regression.
                 pre-select 15 features. R package glmnet, lasso. ROC.
                 pop15000 14 functions in function set. PMID:24498380",

Genetic Programming entries for Daniel Beck James A Foster