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Current Breathomics - a Review on Data Pre-processing Techniques and Machine Learning in Metabolomics Breath Analysis

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons44594

Hauschild,  Anne-Christin
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44085

Baumbach,  Jan
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

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Smolinska, A., Hauschild, A.-C., Fijten, R., Dallinga, J., Baumbach, J., & van Schooten, F. (2014). Current Breathomics - a Review on Data Pre-processing Techniques and Machine Learning in Metabolomics Breath Analysis. Journal of Breath Research, 8(2): 027105. doi:10.1088/1752-7155/8/2/027105.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0024-66CC-F
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.