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A modular computational framework for automated peak extraction from ion mobility spectra

Overview of attention for article published in BMC Bioinformatics, January 2014
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Title
A modular computational framework for automated peak extraction from ion mobility spectra
Published in
BMC Bioinformatics, January 2014
DOI 10.1186/1471-2105-15-25
Pubmed ID
Authors

Marianna D’Addario, Dominik Kopczynski, Jörg Ingo Baumbach, Sven Rahmann

Abstract

An ion mobility (IM) spectrometer coupled with a multi-capillary column (MCC) measures volatile organic compounds (VOCs) in the air or in exhaled breath. This technique is utilized in several biotechnological and medical applications. Each peak in an MCC/IM measurement represents a certain compound, which may be known or unknown. For clustering and classification of measurements, the raw data matrix must be reduced to a set of peaks. Each peak is described by its coordinates (retention time in the MCC and reduced inverse ion mobility) and shape (signal intensity, further shape parameters). This fundamental step is referred to as peak extraction. It is the basis for identifying discriminating peaks, and hence putative biomarkers, between two classes of measurements, such as a healthy control group and a group of patients with a confirmed disease. Current state-of-the-art peak extraction methods require human interaction, such as hand-picking approximate peak locations, assisted by a visualization of the data matrix. In a high-throughput context, however, it is preferable to have robust methods for fully automated peak extraction.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 28 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 5 18%
Researcher 5 18%
Student > Ph. D. Student 5 18%
Student > Bachelor 4 14%
Student > Doctoral Student 3 11%
Other 4 14%
Unknown 2 7%
Readers by discipline Count As %
Computer Science 6 21%
Medicine and Dentistry 4 14%
Biochemistry, Genetics and Molecular Biology 2 7%
Agricultural and Biological Sciences 2 7%
Physics and Astronomy 2 7%
Other 9 32%
Unknown 3 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 January 2014.
All research outputs
#19,007,097
of 21,361,101 outputs
Outputs from BMC Bioinformatics
#6,511
of 6,928 outputs
Outputs of similar age
#247,552
of 287,531 outputs
Outputs of similar age from BMC Bioinformatics
#14
of 17 outputs
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