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Author, Editor

Author(s):

Hauschild, Anne-Christin
Schneider, Till
Pauling, Josch
Rupp, Kathrin
Jang, Mi
Baumbach, Jörg Ingo
Baumbach, Jan

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Not MPG Author(s):

Schneider, Till
Rupp, Kathrin
Jang, Mi
Baumbach, Jörg Ingo

BibTeX cite key*:

Hauschild2012b

Title

Title*:

Computational Methods for Metabolomic Data Analysis of Ion Mobility Spectrometry Data - Reviewing the State of the Art


metabolites-02-00733.pdf (2821.79 KB)

Journal

Journal Title*:

Metabolites

Journal's URL:

http://www.mdpi.com/journal/metabolites

Download URL
for the article:

http://www.mdpi.com/2218-1989/2/4/733/pdf

Language:

English

Publisher

Publisher's
Name:

MDPI

Publisher's URL:


Publisher's
Address:

Basel

ISSN:

2218-1989

Vol, No, Year, pp.

Volume:

2

Number:


Month:


Year*:

2012

Pages:

733-755

Number of VG Pages:


Sequence Number:


DOI:

10.3390/metabo2040733

Abstract, Links, (C)

Note:


(LaTeX) Abstract:

Ion mobility spectrometry combined with multi-capillary columns (MCC/IMS) is a well known technology for detecting volatile organic compounds (VOCs). We may utilize MCC/IMS for scanning human exhaled air, bacterial colonies or cell lines, for example. Thereby we gain information about the human health status or infection threats. We may further study the metabolic response of living cells to external perturbations. The instrument is comparably cheap, robust and easy to use in every day practice. However, the potential of the MCC/IMS methodology depends on the successful application of computational approaches for analyzing the huge amount of emerging data sets. Here, we will review the state of the art and highlight existing challenges. First, we address methods for raw data handling, data storage and visualization. Afterwards we will introduce de-noising, peak picking and other pre-processing approaches. We will discuss statistical methods for analyzing correlations between peaks and diseases or medical treatment. Finally, we study up-to-date machine learning techniques for identifying robust biomarker molecules that allow classifying patients into healthy and diseased groups. We conclude that MCC/IMS coupled with sophisticated computational methods has the potential to successfully address a broad range of biomedical questions. While we can solve most of the data pre-processing steps satisfactorily, some computational challenges with statistical learning and model validation remain.

URL for the Abstract:

http://www.mdpi.com/2218-1989/2/4/733

Categories / Keywords:

ion mobility spectrometry, clinical diagnostics, peak detection, statistics, statistical learning methods, metabolomics, volatile organic compounds

HyperLinks / References / URLs:


Copyright Message:


Personal Comments:


Download
Access Level:

Public

Correlation

MPG Unit:

Max-Planck-Institut für Informatik



MPG Subunit:

Computational Biology and Applied Algorithmics

MPG Subsubunit:

Computational Systems Biology

Appearance:

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



BibTeX Entry:

@MISC{Hauschild2012b,
AUTHOR = {Hauschild, Anne-Christin and Schneider, Till and Pauling, Josch and Rupp, Kathrin and Jang, Mi and Baumbach, J{\"o}rg Ingo and Baumbach, Jan},
TITLE = {Computational Methods for Metabolomic Data Analysis of Ion Mobility Spectrometry Data - Reviewing the State of the Art},
JOURNAL = {Metabolites},
PUBLISHER = {MDPI},
YEAR = {2012},
VOLUME = {2},
PAGES = {733--755},
ADDRESS = {Basel},
ISBN = {2218-1989},
DOI = {10.3390/metabo2040733},
}


Entry last modified by Anja Becker, 02/14/2013
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Editor(s)
[Library]
Created
01/16/2013 05:02:54 PM
Revision
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Editor
Anja Becker
Anne-Christin Hauschild


Edit Date
14.02.2013 11:17:41
01/16/2013 05:02:54 PM


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