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Author, Editor

Author(s):

Hauschild, Anne-Christin
Kopczynski, Dominik
D'Addario, Marianna
Baumbach, Joerg Ingo
Rahmann, Sven
Baumbach, Jan

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Not MPG Author(s):

Kopczynski, Dominik
D'Addario, Marianna
Baumbach, Joerg Ingo
Rahmann, Sven

BibTeX cite key*:

Hauschild2012c

Title

Title*:

Peak Detection Method Evaluation for Ion Mobility Spectrometry by using Machine Learning Approaches

Journal

Journal Title*:

Metabolites

Journal's URL:

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

Download URL
for the article:

http://www.mdpi.com/2218-1989/3/2/277/pdf

Language:

English

Publisher

Publisher's
Name:


Publisher's URL:


Publisher's
Address:


ISSN:


Vol, No, Year, pp.

Volume:

3

Number:

2

Month:

April

Year*:

2013

Pages:

277-293

Number of VG Pages:


Sequence Number:


DOI:


Abstract, Links, (C)

Note:


(LaTeX) Abstract:

Ion mobility spectrometry with pre-separation by multi-capillary columns (MCC/IMS) has become an established inexpensive, non-invasive bioanalytics technology for detecting volatile organic compounds (VOCs) with various metabolomics applications in medical research. To pave the way for this technology towards daily usage in medical practice, different steps still have to be taken. With respect to modern biomarker research, one of the most important tasks is the automatic classification of patient-specific data sets into different groups, healthy or not, for instance. Although sophisticated machine learning methods exist, an inevitable preprocessing step is reliable and robust peak detection without manual intervention. In this work we evaluate four state-of-the-art approaches for automated IMS-based peak detection: local maxima search, watershed transformation with IPHEx, region-merging with VisualNow, and peak model estimation (PME).We manually generated Metabolites 2013, 3 278 a gold standard with the aid of a domain expert (manual) and compare the performance of the four peak calling methods with respect to two distinct criteria. We first utilize established machine learning methods and systematically study their classification performance based on the four peak detectors’ results. Second, we investigate the classification variance and robustness regarding perturbation and overfitting. Our main finding is that the power of the classification accuracy is almost equally good for all methods, the manually created gold standard as well as the four automatic peak finding methods. In addition, we note that all tools, manual and automatic, are similarly robust against perturbations. However, the classification performance is more robust against overfitting when using the PME as peak calling preprocessor. In summary, we conclude that all methods, though small differences exist, are largely reliable and enable a wide spectrum of real-world biomedical applications.

URL for the Abstract:

http://www.mdpi.com/2218-1989/3/2/277

Categories / Keywords:

mcc/ims, peak detection, machine learning, ion mobility spectrometry, spectrum analysis

HyperLinks / References / URLs:


Copyright Message:


Personal Comments:


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Access Level:

Internal

Correlation

MPG Unit:

Max-Planck-Institut für Informatik



MPG Subunit:

Computational Biology and Applied Algorithmics

Audience:

experts only

Appearance:

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



BibTeX Entry:

@MISC{Hauschild2012c,
AUTHOR = {Hauschild, Anne-Christin and Kopczynski, Dominik and D'Addario, Marianna and Baumbach, Joerg Ingo and Rahmann, Sven and Baumbach, Jan},
TITLE = {Peak Detection Method Evaluation for Ion Mobility Spectrometry by using Machine Learning Approaches},
JOURNAL = {Metabolites},
YEAR = {2013},
NUMBER = {2},
VOLUME = {3},
PAGES = {277--293},
MONTH = {April},
}


Entry last modified by Anne-Christin Hauschild, 02/25/2014
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Editor(s)
[Library]
Created
01/20/2014 04:05:51 PM
Revisions
2.
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Editor(s)
Anne-Christin Hauschild
Anne-Christin Hauschild
Anne-Christin Hauschild

Edit Dates
01/20/2014 04:08:19 PM
01/20/2014 04:07:44 PM
01/20/2014 04:05:51 PM