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Author, Editor

Author(s):

Hauschild, Anne-Christin
Baumbach, Jörg Ingo
Baumbach, Jan

dblp
dblp
dblp

Not MPG Author(s):

Baumbach, Jörg Ingo

BibTeX cite key*:

Hauschild2012

Title

Title*:

Integrated statistical learning of metabolic ion mobility spectrometry profiles for pulmonary disease identification

Journal

Journal Title*:

Genetics and Molecular Research

Journal's URL:


Download URL
for the article:

http://www.geneticsmr.com//year2012/vol11-3/pdf/gmr2065.pdf

Language:

English

Publisher

Publisher's
Name:

Fundação de Pesquisas Científicas

Publisher's URL:

http://www.geneticsmr.com/

Publisher's
Address:

Ribeirão Preto, SP | Brasil

ISSN:

1676-5680

Vol, No, Year, pp.

Volume:

11

Number:

3

Month:


Year*:

2012

Pages:

2733-2744

Number of VG Pages:


Sequence Number:


DOI:

10.4238/2012.July.10.17

Abstract, Links, (C)

Note:


(LaTeX) Abstract:

Exhaled air carries information on human health status. Ion mobility spectrometers combined with a multi-capillary column (MCC/IMS) is a well-known technology for detecting volatile organic compounds (VOCs) within human breath. This technique is relatively inexpensive, robust and easy to use in every day practice. However, the potential of this methodology depends on successful application of computational approaches for finding relevant VOCs and classification of patients into disease-specific profile groups based on the detected VOCs. We developed an integrated state-of-the-art system using sophisticated statistical learning techniques for VOC-based feature selection and supervised classification into patient groups. We analyzed breath data from 84 volunteers, each of them either suffering from chronic obstructive pulmonary disease (COPD), or both COPD and bronchial carcinoma (COPD + BC), as well as from 35 healthy volunteers, comprising a control group (CG). We standardized and integrated several statistical learning methods to provide a broad overview of their potential for distinguishing the patient groups.
We found that there is strong potential for separating MCC/IMS
chromatograms of healthy controls and COPD patients (best accuracy COPD vs CG: 94%). However, further examination of the impact of
bronchial carcinoma on COPD/no-COPD classification performance
is necessary (best accuracy CG vs COPD vs COPD + BC: 79%). We
also extracted 20 high-scoring VOCs that allowed differentiating
COPD patients from healthy controls. We conclude that these statistical learning methods have a generally high accuracy when applied to wellstructured, medical MCC/IMS data.

URL for the Abstract:

http://www.geneticsmr.com/articles/1753?quicktabs_1=1

Categories / Keywords:

Ion mobility spectrometry, Machine learning, Chronic obstructive pulmonary disease, Bronchial carcinoma, Feature selection

HyperLinks / References / URLs:


Copyright Message:


Personal Comments:


Download
Access Level:

Intranet

Correlation

MPG Unit:

Max-Planck-Institut für Informatik



MPG Subunit:

Computational Biology and Applied Algorithmics

MPG Subsubunit:

Computational Systems Biology

Audience:

not specified

Appearance:

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



BibTeX Entry:

@MISC{Hauschild2012,
AUTHOR = {Hauschild, Anne-Christin and Baumbach, J{\"o}rg Ingo and Baumbach, Jan},
TITLE = {Integrated statistical learning of metabolic ion mobility spectrometry profiles for pulmonary disease identification},
JOURNAL = {Genetics and Molecular Research},
PUBLISHER = {Fundação de Pesquisas Científicas},
YEAR = {2012},
NUMBER = {3},
VOLUME = {11},
PAGES = {2733--2744},
ADDRESS = {Ribeirão Preto, SP | Brasil},
ISBN = {1676-5680},
DOI = {10.4238/2012.July.10.17},
}


Entry last modified by Anja Becker, 02/14/2013
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Editor(s)
[Library]
Created
01/16/2013 04:31:35 PM
Revision
1.
0.


Editor
Anja Becker
Anne-Christin Hauschild


Edit Date
14.02.2013 11:15:49
01/16/2013 04:31:35 PM