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Author, Editor

Author(s):

Smolinska, Agnieszka
Hauschild, Anne-Christin
Fijten, Rianne
Dallinga, Jan
Baumbach, Jan
van Schooten, Frederik

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Not MPG Author(s):

Smolinska, Agnieszka
Fijten, Rianne
Dallinga, Jan
van Schooten, Frederik

BibTeX citekey*:

Hauschild2014

Title, Booktitle

Title*:

Current breathomics – A review on data pre-processing techniques and machine learning in metabolomics breath analysis

Vol, No, pp., Year

Month:


Year:

2014

Language:

English

Pages:


Abstract, Links, ©

Note:


LaTeX Abstract:

We define breathomics as the metabolomics study of exhaled air. It is a
strongly emerging metabolomics research field that mainly focuses on
health-related Volatile Organic Compounds (VOCs). Since the composition
of these compounds varies depending on health status, breathomics holds
great promise as non-invasive diagnostic tool. Thusthe main aim of
breathomics is to find the patterns of VOCs relatedto deviant (for instance
inflammatory) metabolic processes occurring e.g. inthe human body.
Consequently, methods for recording VOCs in exhaledair for diagnosis and
monitoring health status gained increased attentionover the last years. As
a result, measuring breath air high-throughput and in high resolution has
enormously developed. Yet machine learning solutions for fingerprinting
VOCs profiles in the breathomics research field arestill in their infancy.
Therefore in this review/tutorial we describe the current state of the art in
data pre-processing and analysis. We start with detailed pre-processing
pipelines for breathomics data obtained from Gas-Chromatography Mass
Spectrometry and Ion Mobility Spectrometer coupled to Multi-Capillary
Columns. The final result of such pipelines is a matrix containing the
relative abundances of a set of VOCs for a group ofpatients under different
conditions (e.g. disease stage, treatment). Independently of the utilized
analytical technique the most important question: “Which VOCs are
discriminatory”, remains the same. Hence, in the main part of our
review/tutorial we focus on several modern machine learning methods
(multivariate statistics). We demonstrate the advantages as well the
drawbacks of such techniques. We aim to help the breath analysis
community to understand when and how one can profitfrom a certain
method. In parallel, we hope to make the community aware of the
existing, yet in breathomics unmet research data fusion methods.

Categories / Keywords:

GC-MS, MCC-IMS, exhaled air, multivariate analysis, volatile organic compounds (VOCs)

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MPG Unit:

Max-Planck-Institut für Informatik



MPG Subunit:

Computational Biology and Applied Algorithmics

Audience:

experts only

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MPII WWW Server, MPII FTP Server, MPG publications list, university publications list, working group publication list, Fachbeirat, VG Wort

BibTeX Entry:
@UNPUBLISHED{Hauschild2014,
AUTHOR = {Smolinska, Agnieszka and Hauschild, Anne-Christin and Fijten, Rianne and Dallinga, Jan and Baumbach, Jan and van Schooten, Frederik},
TITLE = {Current breathomics – A review on data pre-processing techniques and machine learning in metabolomics breath analysis},
YEAR = {2014},
}


Entry last modified by Anne-Christin Hauschild, 01/07/2015
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Created
01/20/2014 04:45:20 PM
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Editor
Anne-Christin Hauschild



Edit Date
01/20/2014 04:45:20 PM