Machine learning classifiers provide insight into the relationship between microbial communities and bacterial vaginosis

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@Article{Beck:2015:BDM,
  author =       "Daniel Beck and James A. Foster",
  title =        "Machine learning classifiers provide insight into the
                 relationship between microbial communities and
                 bacterial vaginosis",
  journal =      "BioData Mining",
  year =         "2015",
  volume =       "8",
  number =       "23",
  month =        "12 " # aug,
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1186/s13040-015-0055-3",
  size =         "9 pages",
  abstract =     "Background: Bacterial vaginosis (BV) is a disease
                 associated with the vagina microbiome. It is highly
                 prevalent and is characterized by symptoms including
                 odour, discharge and irritation. No single microbe has
                 been found to cause BV. In this paper we use random
                 forests and logistic regression classifiers to model
                 the relationship between the microbial community and
                 BV. We use subsets of the microbial community features
                 in order to determine which features are important to
                 the classification models.

                 Results: We find that models generated using logistic
                 regression and random forests perform nearly
                 identically and identify largely similar important
                 features. Only a few features are necessary to obtain
                 high BV classification accuracy. Additionally, there
                 appears to be substantial redundancy between the
                 microbial community features.

                 Conclusions: These results are in contrast to a
                 previous study in which the important features
                 identified by the classifiers were dissimilar. This
                 difference appears to be the result of using different
                 feature importance measures. It is not clear whether
                 machine learning classifiers are capturing patterns
                 different from simple correlations.",
}

Genetic Programming entries for Daniel Beck James A Foster

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