- 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.",
