Machine Learning Techniques Accurately Classify Microbial Communities by Bacterial Vaginosis Characteristics
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Beck:2014:PLoSONE,
-
author = "Daniel Beck and James A. Foster",
-
title = "Machine Learning Techniques Accurately Classify
Microbial Communities by Bacterial Vaginosis
Characteristics",
-
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 www.ncbi.nlm.nih.gov",
-
publisher = "Public Library of Science",
-
oai = "oai:pubmedcentral.nih.gov:3912131",
-
URL = "http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3912131",
-
URL = "http://dx.doi.org/10.1371/journal.pone.0087830",
-
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
Citations