Carotta: Revealing Hidden Confounder Markers in Metabolic Breath Profiles.

ABSTRACT: Computational breath analysis is a growing research area aiming at identifying volatile organic compounds (VOCs) in human breath to assist medical diagnostics of the next generation. While inexpensive and non-invasive bioanalytical technologies for metabolite detection in exhaled air and bacterial/fungal vapor exist and the first studies on the power of supervised machine learning methods for profiling of the resulting data were conducted, we lack methods to extract hidden data features emerging from confounding factors. Here, we present Carotta, a new cluster analysis framework dedicated to uncovering such hidden substructures by sophisticated unsupervised statistical learning methods. We study the power of transitivity clustering and hierarchical clustering to identify groups of VOCs wit

SUBMITTER: Hauschild AC 

PROVIDER: S-EPMC4495376 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature