An improved machine learning pipeline for urinary volatiles disease detection: Diagnosing diabetes.

ABSTRACT:

Motivation

The measurement of disease biomarkers in easily-obtained bodily fluids has opened the door to a new type of non-invasive medical diagnostics. New technologies are being developed and fine-tuned in order to make this possibility a reality. One such technology is Field Asymmetric Ion Mobility Spectrometry (FAIMS), which allows the measurement of volatile organic compounds (VOCs) in biological samples such as urine. These VOCs are known to contain a range of information on the relevant person's metabolism and can in principle be used for disease diagnostic purposes. Key to the effective use of such data are well-developed data processing pipelines, which are necessary to extract the most useful data from the complex underlying biological structure.

Results

In this st

SUBMITTER: Martinez-Vernon AS 

PROVIDER: S-EPMC6160042 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature